Amazon.com
Artificial Intelligence: A Modern Approach introduces basic ideas in artificial intelligence from the perspective of building intelligent agents, which the authors define as "anything that can be viewed as perceiving its environment through sensors and acting upon the environment through effectors." This textbook is up-to-date and is organized using the latest principles of good textbook design. It includes historical notes at the end of every chapter, exercises, margin notes, a bibliography, and a competent index. Artificial Intelligence: A Modern Approach covers a wide array of material, including first-order logic, game playing, knowledge representation, planning, and reinforcement learning.
Book Description
The long-anticipated revision of this best-selling book offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For those interested in artificial intelligence.
Customer Reviews:
Highly recommended.......2007-09-28
I am half way through and I like it so far. Frankly I am puzzled by other reviewers complaining about "lack of real code examples", they clearly have not read the book carefully: it comes with tons of sample code (online) written in different languages, publishers/authors simply did not want to waste the precious real estate. The book is nearly a thousand pages already.
Otherwise this is a great CS book. Yes there is some math in it, but don't be scared - there is an appendix with all necessary mathematical background you'll need (and you don't need much). I was surprised to see so much historical references in this book, it teaches you not just about most major branches of AI, but also about how they started and where originated from in a "problem -> solution" form. For instance when they talk about genetic algorithms they actually go ahead and write a comprehensive comparison of analogies between biological evolution, genes and their computer-generated counterparts referencing the original work of Darwin and others.
If you're into AI, applied mathematics or computer science, I have no doubt you'll enjoy this book: it's not too focused on something specific (and something you'd need a PhD to understand) while not too shallow and covers fairly wide spectrum of AI problems, including (!) ethical and philosophical issues like "what happens if we succeed?"
Highly recommended.
Worth a million.......2007-09-26
An author of this book is said to have commented that its writing has made him a millionaire. It is used in over 1000 universities for a simple reason, it is good. The book uses the concept of an agent to unify the formerly fragmented field of AI and to link together concepts as diverse as logic programming and ethics. It is very easy to read and touches every area of modern research interest I can think of. The problems have a nice variety of difficulties (although there are no worked-out solutions in the book) and provide a mix between theory and practice, introducing the careful student to concepts and papers not developed in the main text of the chapter. The bibliography is well laid-out and provides useful depth (one of my current research interests was sparked by reading one of the referenced papers in the 2nd chapter).
My only complaint so far (not having finished the entire book) is that some of the definitions in chapter 17's whirlwind introduction to game theory were a little vague. But, a quick look at some other sources clarified things immensely.
It is rare to find a textbook as interesting and clear as this one. If a professor is requiring that you read it, consider yourself fortunate. If you are thinking of reading it yourself, you also are blessed. Look forward to many pleasant evenings.
Worthwhile.......2007-08-23
This book is very worthwhile if you are looking into AI with the purpose of understanding the various techniques, etc. It gives a really good background, and I find it useful. The only changes I would have made (for me, not everyone else) would be to include a few short chapters on second order logic, and the basic mathematics and math terms used in the book. It assumes more knowlege in math than I have. It would also be nice if it had a "recommended reading" page, listing those texts that would be useful for an AI beginner to review in order to understand the math and logic referred to. (My degree was in 79, and I only had a very basic calculus course...not too deep...I can diffentiate, but that is all).
It would just be nice if they could list reference books for people who are math nieve.
Thorough book.......2007-01-04
This book was very thorough in many facets of Artificial Intelligence. It was a tremendous help in the class I took and will be a great reference for future years.
Thick, informative & loads of diagrams.......2007-01-04
Useful book which at first explains in great detail the history, foundations for AI and the different approaches AI takes on. Your course will probably not use all the material in the book but still makes for interesting reading. Would recommend if you can pick up a copy cheap.
Average customer rating:
- Down the Rabbit Hole...
- Come one, come all
- Bound with the "braid"?
- Excellent book!
- "This sentence is false."
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Godel, Escher, Bach: An Eternal Golden Braid
Douglas R. Hofstadter
Manufacturer: Basic Books
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ASIN: 0465026567 |
Amazon.com
Twenty years after it topped the bestseller charts, Douglas R. Hofstadter's Gödel, Escher, Bach: An Eternal Golden Braid is still something of a marvel. Besides being a profound and entertaining meditation on human thought and creativity, this book looks at the surprising points of contact between the music of Bach, the artwork of Escher, and the mathematics of Gödel. It also looks at the prospects for computers and artificial intelligence (AI) for mimicking human thought. For the general reader and the computer techie alike, this book still sets a standard for thinking about the future of computers and their relation to the way we think.
Hofstadter's great achievement in Gödel, Escher, Bach was making abstruse mathematical topics (like undecidability, recursion, and 'strange loops') accessible and remarkably entertaining. Borrowing a page from Lewis Carroll (who might well have been a fan of this book), each chapter presents dialogue between the Tortoise and Achilles, as well as other characters who dramatize concepts discussed later in more detail. Allusions to Bach's music (centering on his Musical Offering) and Escher's continually paradoxical artwork are plentiful here. This more approachable material lets the author delve into serious number theory (concentrating on the ramifications of Gödel's Theorem of Incompleteness) while stopping along the way to ponder the work of a host of other mathematicians, artists, and thinkers.
The world has moved on since 1979, of course. The book predicted that computers probably won't ever beat humans in chess, though Deep Blue beat Garry Kasparov in 1997. And the vinyl record, which serves for some of Hofstadter's best analogies, is now left to collectors. Sections on recursion and the graphs of certain functions from physics look tantalizing, like the fractals of recent chaos theory. And AI has moved on, of course, with mixed results. Yet Gödel, Escher, Bach remains a remarkable achievement. Its intellectual range and ability to let us visualize difficult mathematical concepts help make it one of this century's best for anyone who's interested in computers and their potential for real intelligence. --Richard Dragan
Topics Covered: J.S. Bach, M.C. Escher, Kurt Gödel: biographical information and work, artificial intelligence (AI) history and theories, strange loops and tangled hierarchies, formal and informal systems, number theory, form in mathematics, figure and ground, consistency, completeness, Euclidean and non-Euclidean geometry, recursive structures, theories of meaning, propositional calculus, typographical number theory, Zen and mathematics, levels of description and computers; theory of mind: neurons, minds and thoughts; undecidability; self-reference and self-representation; Turing test for machine intelligence.
Book Description
Winner of the Pulitzer Prize, this book applies Godel's seminal contribution to modern mathematics to the study of the human mind and the development of artificial intelligence.
Customer Reviews:
Down the Rabbit Hole..........2007-05-18
This is a difficult book.
Difficult to read. Difficult to understand. And, I'm finding, difficult to review. What's it about? Good question. The author, himself, isn't very clear on this point, describing it as "a metaphorical fugue on minds and machines in the spirit of Lewis Carroll." I'm not sure I can do better than that. I will tell you this, however: if the book has a "point," it does seem to be that man's consciousness is ultimately mechanical and, therefore, that there is no reason that machines cannot finally be intelligent in the same sense that man is. (And, in fact, be as man in just about every internal way.)
While I take issue with this conclusion, and some of Hofstadter's reasoning along the way, I don't think that my debating his points is the basis on which a prospective reader should decide whether or not to pick up this book. Instead, the prospective reader should know: that this is a lengthy and deep work. It will take a *long* time to read properly, and most readers should not read more than a chapter a day. Many of the sections, and especially the various dialogues that preface the chapters, are quite clever. (These dialogues are usually between Achilles and the Tortoise, of Zeno's paradoxes, and their friends.) Some of the chapters grow incredibly technical. The subject matters vary, wildly and rapidly, and there will be points in reading where you will question your investment.
In the end, you will feel good for having pushed through the hard bits. It will coalesce, more or less, into a whole. Whether you finally agree with Hofstadter's conclusions or not, you'll have learned much and thought about important topics you might otherwise not have.
A good book, certainly not for everyone... but, if you're the "right" audience--someone deeply interested in questions of intelligence, mathematics, computer science and free will, and possessed of a bit of an ironic sense of humor--then this book cannot be recommended highly enough.
Five stars, for the work it represents, and the doors it opens to the reader.
Come one, come all.......2007-05-16
As you can see from other reviews, people tend to walk away from this book with a variety of different impressions. Math, Art, Logic, Philosophy, Human Perception and Thought, it has it all. This is second to the Bible in my collection as a book I've read multiple times and can still come back to a read again for even more insight and perspective.
Bound with the "braid"?.......2007-05-14
Can someone tell me, in plain English, what this book is about? On the little matter of determinism--is he for it or against it? He does not seem to have come to praise Godel, Escher, Bach for their strangeness but rather to bury strangeness and its resistance to materialism. He seems to be saying that strangeness is hardwired and can be programmed into a formal system by someone who sees it for what it is--in short, that computers will some day rise to the level of consiousness and self-reference. But wouldn't such a system be curved in upon itself and lack strangeness? If strangeness could be hard-wired into AI, would it still seem strange? Nothingness annihilates strangeness, but then the absense of strangeness is the actual limit of the theories of value seen in those who follow Heidegger. In order to eliminate the difference between soul and matter, they must give up the resistance of soul to the limitations of material existence; at which point "strangeness" becomes a matter of verbal virtuosity and conceptual sleight of hand. "Strangeness" becomes the same thing as cleverness. Or am I misreading this fascinating book?
Excellent book!.......2007-05-14
Hofstadter combines the awe in math, music, art, artificial intelligence, language and computers into one big book called GEB. Its takes the reader on an ecstatic journey with a clever use of parallels between the structure of math, music and finite but endless loops that appear in Escher's works. Dialogs between Achilles and Tortoise are very interesting.
"This sentence is false.".......2007-03-19
A simple example of recursiveness in music is the song "row, row, row your boat." The song becomes recursive as each new line is started when the original line makes it to "gently down the stream." In this way, we have a musical example of the artistic portrayals of Maurits Cornelius Escher whose paintings invariably fosuc on recursive visual themes such as two hands in the process of drawing each other.
In each case, the depiction challenges our ability to pidgeon hole the phenomenon we are examining. Which line is the harmony, which is the melody in "row, row, row your boat"? Which hand is drawing which in the Escher print?
Liguistically, the same effect occurs when we examine the statement "This sentence is false." Logically if we accept the statement at its face value being false then it becomes an accurate representation (in that it correctly asserts its falseness). On the other hand, we are also drawn to the conclusion that the statement is true (again because it is self referentially accurate).
Ultimately, we are forced to logically conclude that we can neither bracket the statement "This sentence is false" with either all true statements or all untrue statements. As indicated previously, like the song "row, row, row your boat" or an Escher painting, the sentence defies pidgeon holing owing to its recursive quality.
Back in 1931, Kurt Godel shocked the mathematics community with his assertion that mathematically consistent systems themselves necessarily produce formally undecideable propositions (the math equivalent of "This sentence is false"). At the time of presenting his paper, it was Godel's intent to demonstrate the unique nature of human intellect because if we can resolve undecideable propositions then there must be something unique to the process of human intellect.
While Godel certainly brought undeniable genius to the creation of his theorem, it doesn't follow that the theorem proves the uniqueness of human intellect. And the reason Godel's theorem doesn't prove the uniqueness of human intellect is because its logical limitations are our own.
Just as Godelian mathematics can't prove undecideable propositions, neither can we "prove" them.
However, we can "believe" undecideable propositions. (In this regard, two easy cases in point are Goldbach's conjecture -- that all even numbers are the sum of two primes -- and that parallel lines really are parallel.) In this way, Godel's theorem, in combination with modern research on artificial intelligence, shows that it is the emotive side of reason that defies the strict logical limitations of Godelian constructs.
These hard won discoveries have combined to make for some surprising findings.
Probably the first among these most observable to the general public through the misconception of science fiction is that emotion somehow stagnates the operation of intellect. In this way, it was HAL 9000's personality as much as the creepiness of that personality that was surprising to 1968 movie goers watching "2001: A Space Odessy." As demonstrated in the movie, it was the fact of HAL's emotive connections with the ongoing actions of his crew that prompted "him" to formulate and act on plans.
Second, modern research has shown that human intellect is not best characterized as being a "blank slate" but rather a delicate combination of various systems that survey reality in the own ways. An easy example is the human eye which uses a combination of three different light cones to measure redness, greenness and blueness. It is the relative comparisons of these cone findings that nudges your visual perception to observe the color of an object. At the intellectual level, one system is entirely devoted to our understanding of artifacts. How do they work? How can they be modified for use in a situation? Another system comprehends animate creatures. Yet another system recognizes faces. Still another system is devoted to language acquisition.
And significantly all these systems acquire information emotively. We see the face of a parent and emotively appreciate it (unless we suffer from a particular cognitive disorder that has disabled our ability to do so as for example discussed by Oliver Sacks in his great book "The man who mistook his wife for a hat"). We remember a concept learned and emotively evaluate it. In this way, freedom, communism, taxes are not just intellectual constructs but ideas that spark real feelings on our part.
In creating Godel, Escher, Bach, Douglas Hofstadter displayed true genius in linking three domains wherein recursiveness seems to play such a pivatol role. As he indicated, they are three shadows cast from the same source.
In re-concluding this book, however, I couldn't help but think of other possible titles that could be added to a Godel, Escher, Bach type encyclopedia: "Phi, Di Vinci, Bach" -- the story of the "golden ratio" of phi which plays a role in Di Vinci's art work and as it so happens also in the music of Bach; "Pascal, State Lotteries, Happy Birthday" -- the story of Pascal's wager and how an appreciation of statistics will make us understand why states will never lose money running a state lottery for reasons akin to why relatively small groupings of people will have at least two that share the same birthday; and "Klein, Carroll, Kubrick" -- the story of Oscar Klein's bottle which can resort to the fourth dimensionj to fill itself up and how speculations by the physicist J Richard Gott suggest that Alice and all of us may have originallyu gone down the rabbit hole for a real space odessy through time itself.
The point here is not that Hofstadter was incorrect but (no pun intended) merely incomplete in his survey when he said that Godel's proof, Escher's paintings and Bach's music were but three shadows cast from the same source. The point here is that -- properly examined -- those three shadows, together with the encyclopedia I've suggested, would direct us not only to the origins of consciousness but also the origin of origins itself.
Average customer rating:
- Perfect
- Excellent book for pattern analysis and classification!
- The book should change its title
- Ok, but too much math destroys the intuition...
- The best Pattern Recognition textbook I know
|
Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop
Manufacturer: Springer
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ASIN: 0387310738 |
Book Description
The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.
This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.
Coming soon:
*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)
*For instructors, worked solutions to remaining exercises from the Springer web site
*Lecture slides to accompany each chapter
*Data sets available for download
Customer Reviews:
Perfect.......2007-10-17
This book is very useful as an introduction to Machine Learning as well as a reference book for more advanced topics.
Excellent book for pattern analysis and classification!.......2007-10-01
Excellent book for pattern analysis and classification! It begins with basic data curve fitting, linear classification models and ends with combining models (tree-based models, graphical models, etc.). Contains great number of examples and exercises. Very good introductory for beginners in pattern analysis, excellent companion for academics and researchers.
The book should change its title.......2007-09-25
This book (PRML) should be re-titled as "PRML: a bayesian approach". Yes, bayesian approach is very useful for machine learning, and sometimes the final goal of learning is to maximize some sort of posterior probability. However, if the author is such a huge fun of bayes statistics, please tell perspective readers in a clear way. Emphasize bayes aspects too much really hurt the quality of this book as a general-purpose textbook of machine learning.
For a better textbook of machine learning, I recommend:
1) The elements of statistical learning (perhaps this book a little hard for beginner in this field -- but as least better than PRML -- you can compare their chapters about linear regression to see which one is better).
2) Pattern classification (focus on classification, not regression. Also not very easy -- anyway, machine learning is not an easy field ^_^).
3) Machine Learning (a little old, but great for beginner.)
These three book also mention bayesian statistics, but in a proper way. If you have some experience in machine learning and have engineering-level math background, just choose the 1) or 2). If you are completely a beginner, first take a glance on 3), and then go to 1) or 2).
Finally, if you want a book that discusses machine learning purely from bayesian perspective, PRML is good.
Ok, but too much math destroys the intuition..........2007-09-09
This book is a fairly thorough overview of typical topics employed in a graduate machine learning course. However, from page 5 on, expect to see more equations on each page than paragraphs of text (with most of the remaining text explaining the context of the variables within the equations). Now, for someone such as myself who enjoys mathematics, this is not a problem. However, I would not recommend this book for someone with a mathematics background that is in any way weak. Furthermore, there is a more fundamental problem with the presentation of the material that warrants this book no more than a 3-star rating: the simple intuitiveness of the concepts is completely lost within the mathematics. Instead of explaining what variables represent and leaving it to the reader to figure out what is going on, this book could be made much more approachable by simply stating the intuition behind the equations. Take the sum rule, one of the first theorems in the book, for an example of how the author muddles what is effectively a basic and intuitive concept: the book has a fairly lengthy definition of several variables representing concepts such as "the number of observations in which x_ij appears" prior to presenting a summation over all y-variables (a notational convention that the author admits is "cumbersome" on the next page, and states that "there will be no need for such pedantry" as that which he proceeds to perpetrate throughout the book!), while he could have simply presented the simplified sum on the following page (p(X) = sum(p(X,Y), Y)) and it would be immediately clear to most readers what he was attempting to explain. He could also simply state the intuition behind the theorem in English, that summing over every event yields a probability of one, and therefore summing over all events in which a variable appears effectively marginalizes the variable (something he comes close to doing after the presentation of the equation, but by then, the reader's time has already been wasted). Similar examples abound throughout the book, becoming particularly bad during the middle sections, when the techniques begin to become less intuitive.
As another reader mentioned, the author also commits the serious mistake of using pi for a symbol other than the constant or the product operator, which muddles the equations on a skim and forces the reader to refer back to the variable definitions to determine the context.
Having done work in machine learning's applied cousin, data mining, and thus having used many of the techniques presented in the book in actual research, I can't help but think that the presentation of the book's content could be much clearer. When doing work in the field, we can look up the equations as-needed; it is the knowledge of *when* and *how* to apply or extend these techniques that is more important, and that is the area in which I feel this book is lacking.
The best Pattern Recognition textbook I know.......2007-07-17
This book brings the most updated research in this field. The writing stile combines common-sense intuitive explanations with precise mathematical formulations. A lot of colorful figures support the text and help the reader to understand and absorb the described ideas. Short biographies of scientists like Bayes, Laplace, Gauss etc. (which unfortunately substantially drop after the Ch. 2) provide a brief glancing on humans which are behind these great names. The author makes connections between the different chapters, which help the reader to see a wide picture. But don't expect for an easy work. As every deep scientific text it is sometimes fluent and fun, and sometimes demands an effort, rereading the same text again and again, and referring to other references. Personally I feel a great satisfaction when after such an effort the concept became clear to me.
The other useful feature is solved exercises which are available for download from the authors' web site [..]
The main drawback of this book is a relative small amount of detailed examples. As an experienced educator, I know that "a single good example could worth a thousand explanations". It probably will be not an issue with appearance of the practical companion volume (Bishop and Nabney, 2008). The reference to the future (2008) still un-existed publication is unusual, fresh-thinking, and right idea.
With this book C. Bishop continues his "tradition" of writing deep and important scientific books which was started with the "Neural Networks for Pattern Recognition".
A short comment to the reviewer "lew lwndn123", who is deeply disappointed by the fact that this is a textbook. Yes, it is a textbook, and it is clearly written in the "Book Description". It is unfair to "kill" the book just because you didn't really check what you are going to buy, especially you admit that "as a textbook, this is very good text, and deserves 5 starts". I think it will be a decent step if you will correct your review.
Book Description
The Self-Organizing Map (SOM), with its variants, is the most popular artificial neural network algorithm in the unsupervised learning category. Many fields of science have adopted the SOM as a standard analytical tool: in statistics,signal processing, control theory, financial analyses, experimental physics, chemistry and medicine. A new area is organization of very large document collections. The SOM is also one of the most realistic models of the biological brain functions.
This new edition includes a survey of over 2000 contemporary studies to cover the newest results; the case examples were provided with detailed formulae, illustrations and tables; a new chapter on software tools for SOM was written, other chapters were extended or reorganized.
Customer Reviews:
I love this book........2000-03-11
This is a wonderfully written, and excellent book. It assumes only minimal background knowledge but imparts a great deal of insight. I love the way that the author describes this area and the connections with deep and beautiful mathematics.
A very nice 'handbook' of sorts for users of SOMs........1999-08-05
The material is presented clearly and comprehensively from the unique perspective of the SOM originator himself. The inclusion of exhaustive references is particularly useful for the prospective researcher, but, at the risk of sounding ungrateful, I'm curious as to why paper titles were not included in the citations? Overall though, a very good reference.
Average customer rating:
- Broad, but a mess.
- Computer vision
- A good up-to-date reference. Comes with solid introductions to (multi-)camera geometries.
- Not a good book to begin with
- Somewhat opaque
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Computer Vision: A Modern Approach
David A. Forsyth , and
Jean Ponce
Manufacturer: Prentice Hall
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Book Description
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A
CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth.
Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.
Customer Reviews:
Broad, but a mess........2006-10-24
This is, from what I can tell, just about the most complete up-to-date text in the field of computer vision as of late 2006.
But it's a mess.
I'm a PhD student, and have worked my way through more than my fair share of high-level computer science textbooks. This one makes me really appreciate many of them. It reads like a first draft -- overly wordy at times, skipping over important issues, poorly organized... Some concepts that ought to be really simple are made very painful due to what seems to be laziness on the part of the editor. It's like the only people that critiqued this book prior to publication already knew all there is to know about computer vision.
A particularly nasty aspect of this book is that the authors have a horrible habit of including some term in some complex mathematical formula, with no reference whatsoever to that term in the surrounding text! In an explanation of how to use Expectation Maximization in line-fitting, they include a standard-deviation term, with no mention of how you're supposed to choose a value for it other than "...for sigma as before". The only "before" in which the SD (sigma) is mentioned in a similar context that I can find is IN THE PREVIOUS CHAPTER!!!
Anyway, if you want to try to teach yourself vision, don't bother. If you need the book for a class, I'm sorry it's so expensive. Either way, don't expect much.
Computer vision.......2006-07-29
I think this book is the most complete computer vision arguments. In fact it start to speaks from radiometry to steriovision passing by filter uses!!
Good very good
A good up-to-date reference. Comes with solid introductions to (multi-)camera geometries........2006-06-06
First of all, I got a 2003 reprint, and somehow, every single fraction bar (the division sign) is missing in the math expressions for the entire book! Very annoying. I had expected more from Prentice Hall. I advise one to open the book before the return period elapses.
Secondly, I agree to the reviews that say this book being not intuitive. I assume the authors tried to make it concise and came up with a hard read. For example, it explains 2D Fourier analysis in just 3 pages. Of course it's not for first learners. But I get the feeling they could've done a little better in presenting ideas.
The upside is that this book covers quite recent topics. Hence this book may be used as a good survey of the field today.
Personally, I get to grasp rough ideas of the topics I usually don't have time to hit the papers on. Which I appreciate. I knock 2 stars off for overpricing and the misprints in my copy.
[Added in July 2007] I find the chapters on multi-camera geometry, stereopsis, and structure-from-motion are very well written. I'd give it another star for it but the "edit" page won't allow it.
So here it is. It's * * * * -- a four star!
Not a good book to begin with.......2006-02-07
The book fails to explain simple concepts in a clear way. Trying other literature such as 'Shapiro and Stockman's' book is enough to convince yourself that the same concepts can be explained clearly without losing insight.
Somewhat opaque.......2005-12-03
Good point: probably most recent vision book
Bad point: hard to read, missing obvious computer vision topics
Book Description
This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises.
Customer Reviews:
A best book on Statistical Pattern Recognition.......2005-09-13
Multivariate analysis is borrowed to name a NEW subject, Statistical Pattern Recognition (SPR). Many statisticians think it unfair or a shame. In spite of these, it is a good reference book of SPR. :-)
[1] Many contents of this book can be found in any graduate textbook of Multivariate Analysis, for instance, Fisher's linear disciminant, etc.
[2] The book is badly printed. Why not using LaTeX?
[3] Guassian distribution is assumed here and there.
[4] It may be good as a reference book, but definitely not as a textbook.
Standard reference and a classic text but with flaws.......2004-01-20
I do not like to consult this book for the following, quite superficial reason. The book is sloppily produced and proofread
(and the fault is [probably] mainly the publisher's instead of the author's). This manifests itself, e.g., as follows
(1) the typography is flawed (the equations hurt at least my eyes);
(2) at its each appearance, the all-important >
< -sign goes the wrong way.
good coverage for engineers.......2000-08-04
Fukunaga is a standard source for pattern recognition methods often cited in the engineering literature. Covers parametric (particularly linear and quadratic discriminant algorithms) and nonparametric methods (density estimation). It is designed for and popular with engineers. When I was working at Nichols Research Corporation Fukunaga's papers and this book (earlier edition) were often cited as sources to justify the algorithms we used for discrimination problems. In fact Fukunaga had been a consultant to the company (used primarily by the Boston branch of the company where the KENN algorithms were developed). It is a reputable source. I still like Duda and Hart (1972) for good explanations of the fundamental concepts. For statisticians McLachlan's book is now far and away the best source.
Standard Reference in the Field.......2000-04-06
If you are writing a machine learning paper, and need to cite something to support an argument, you can almost always cite Fukunaga. This work is a standard reference in the field. The presentation of most material is very terse, but that is great if you already have a good feel for the material and need to look up some details about some algorithm or technique. There isn't much about neural networks here, but for the rest of the pattern recognition techniques, this is almost always the first place to start. Another strong point for this book is the use of realistic examples, which illustrate many of the statistical techniques.
Amazon.com
Jeff Hawkins, the high-tech success story behind PalmPilots and the Redwood Neuroscience Institute, does a lot of thinking about thinking. In On Intelligence Hawkins juxtaposes his two loves--computers and brains--to examine the real future of artificial intelligence. In doing so, he unites two fields of study that have been moving uneasily toward one another for at least two decades. Most people think that computers are getting smarter, and that maybe someday, they'll be as smart as we humans are. But Hawkins explains why the way we build computers today won't take us down that path. He shows, using nicely accessible examples, that our brains are memory-driven systems that use our five senses and our perception of time, space, and consciousness in a way that's totally unlike the relatively simple structures of even the most complex computer chip. Readers who gobbled up Ray Kurzweil's (The Age of Spiritual Machines and Steven Johnson's Mind Wide Open will find more intriguing food for thought here. Hawkins does a good job of outlining current brain research for a general audience, and his enthusiasm for brains is surprisingly contagious. --Therese Littleton
Book Description
Jeff Hawkins, the man who created the Palm Pilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he is revolutionizing neuro-science and computing with this new look at intelligence itself. In On Intelligence, Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent. The brain is not a computer but a memory system that stores experiences in a way that reflects the true structure of the world, remembering sequences of events and their nested relationships and making predictions based on those memories. It is this memory-prediction system that forms the basis of intelligence, perception, creativity, and even consciousness. Based on this new theory, we can finally build intelligent machines, ones that will likely exceed human ability in surprising and useful ways. Written with acclaimed science writer Sandra Blakeslee and endorsed by a host of scientists and technology experts, On Intelligence reveals how we truly think and how this understanding will transform the technology age.
Download Description
From the inventor of the PalmPilot comes a new and compelling theory of intelligence, brain function, and the future of intelligent machines.
Customer Reviews:
Bridges the gap b/t AI theory and application........2007-10-01
Excellent book. Bridges the gap b/t AI theory and application. Author also has a downloadable programming API based on the research in the book. So you can actually put his theories to the test.
This is how the brain works!.......2007-09-06
This is an amazing book. Especially when I combine the knowledge acquired here with other things I've known and read. I promise you, there's a new book coming out of all this and I'll have it out in 24 months or so. Buy this you'll like it. Buy mine when it comes out, you'll love it!
Best Brain Book.......2007-08-16
Jeff Hawkin's does it ! If you want to know the latest information on how a brain works (and why computers are NOT intelligent) put On Intelligence at the top of your list of 'must reads'...
Paul J. Friday, PhD
Chief, Clinical Psychology
University of Pittsburgh Medical Center - Shadyside
Author: Friday's Laws: How to Become Normal When You're Not and How to Stay Normal When You Are
Amazing.......2007-07-29
Hawkins ideas on how the human brain works are profound. I'm re-reading this book again after finishing it only 4 months ago. With Hierarchical Temporal Memory, Hawkins is not only theorizing how the human brain works, he's also on the path to proving his theories at Numenta. As can be expected, he's building on existing research and theories, which is generally how scientific theories evolve.
Insightful, and ambitious.......2007-07-08
Jeff Hawkins's 'On Intelligence' is an ambitious journey towards a unified theory of cognition and the brain. Varying between the insightful and the minute details that Jeff feels are required to back his theories, the book itself is a mixed read and is probably best aimed at a reader with prior exposure to the field. If you are interested in AI, cognition, or even our physical brains, this is a good read, but take it with a grain of salt - a lot of details and counter-arguments have been conveniently swept under the rug.
Average customer rating:
- Excellent book on estimation/Kalman filter
- best standard book for target tracking system
|
Estimation with Applications to Tracking and Navigation
Yaakov Bar-Shalom ,
X. Rong Li , and
Thiagalingam Kirubarajan
Manufacturer: Wiley-Interscience
ProductGroup: Book
Binding: Hardcover
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Similar Items:
-
Applied Optimal Estimation
-
Beyond the Kalman Filter: Particle Filters for Tracking Applications (Artech House Radar Library)
-
Design and Analysis of Modern Tracking Systems (Artech House Radar Library)
-
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
-
Optimal Filtering (Dover Books on Engineering)
ASIN: 047141655X |
Book Description
Expert coverage of the design and implementation of state estimation algorithms for tracking and navigation
Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations. It explains state estimator design using a balanced combination of linear systems, probability, and statistics.
The authors provide a review of the necessary background mathematical techniques and offer an overview of the basic concepts in estimation. They then provide detailed treatments of all the major issues in estimation with a focus on applying these techniques to real systems. Other features include:
- Problems that apply theoretical material to real-world applications
- In-depth coverage of the Interacting Multiple Model (IMM) estimator
- Companion DynaEst(TM) software for MATLAB(TM) implementation of Kalman filters and IMM estimators
- Design guidelines for tracking filters
Suitable for graduate engineering students and engineers working in remote sensors and tracking, Estimation with Applications to Tracking and Navigation provides expert coverage of this important area.
Customer Reviews:
Excellent book on estimation/Kalman filter.......2004-09-29
I don't usually write online reviews but this book is so clear and useful that I really want to recommend it to others. It is well written with a good outline and summary for every chapter. It also has a pretty diverse range of topics on estimation, including an introductory chapter on basic estimation approaches (e.g., ML, MAP, least squares), and very practical extensions (e.g., state augmentation, square-root filters). Even though I am not in EE and some of the examples are thus not particularly helpful to me, I still find this book one of the best of all the estimation/Kalman filter books out there.
best standard book for target tracking system.......2001-05-09
I think any person who major in target tracking system related to the Kalman filter must see this book. This book present the fundamentals of state estimation theory and the tools for the design of state-of-the-art algorithms for target tracking.
The book covers the basic concepts and estimation techniques for static and dynamic systems, linear and nonlinear, as well as adaptive estiomation. This constitutes a one semester graduate course in estimation theory in an electrical/systems engineering program.
The discussion deals mainly with discrete time estimation algorithms, which are natural for digital computer implementation. The basic state estimation algorithm-the Kalman filter-is presented in discrete as well as in continuous time. The use of the estimation algorithms is illustrated on kinematic motion models because they reveal all the major issues and in particular the subtleties encountered in estimation, and this serves as an introdution to tracking.
Guidelines for tracking filter design-selection of the filter design parameters-are given and illustrated in several examples.
At the end of each chapter, a number of problems that enhance the understanding of the theory and the connection of the theoretical material to the real world are given.
And I have this book as text for my paper.
Book Description
As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work.
The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.
* Algorithmic methods at the heart of successful data miningincluding tried and true techniques as well as leading edge methods
* Performance improvement techniques that work by transforming the input or output
* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualizationin a new, interactive interface
Download Description
Like the popular first edition, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining-including both tried-and-true techniques of the past and Java-based methods at the leading edge of contemporary research. If you're involved at any level in the work of extracting usable knowledge from large collections of data, this clearly written and effectively illustrated book will prove an invaluable resource. Complementing the authors' instruction, including a fully-revised Chapter 8 and 30 new technique sections, is a fully functional platform-independent Java software s
Customer Reviews:
Customer Satisfaction.......2007-09-07
The book I got was in very good condition and the time it took for delivery was also good
A great introduction to data mining and machine learning.......2007-07-24
I bought this book in the hopes that it would help me better explore the data from the Netflix Prize contest, which it did. I had been reading numerous Wikipedia articles, scientific papers, etc. on line and felt it would be useful to have a more general tome on the subject. This book covers many of the common, overarching themes i.e. clustering, neural networks, linear regression, etc. to varing degree. I only wish the examples involved slightly more complex data sets and more pseudo code was provided. I suppose since the book is very closely tied to WEKA, one could always dig through the source code of that application; but I feel that the authors could have provided a bit more of the strictly algorithm relevant code in the book.
Incredibly Useful for theory and Practice.......2007-05-15
This book is by the guys who created WEKA. It covers a wide swath of machine learning without losing a dilligent reader. Then it walks the reader through using WEKA - a fantastically powerful open source tool.
Amazing.
wrong named.......2007-04-20
If "practical" means having a sample project with the theory, the name of this book is incorrectly named. With a big bunch of text or description, it is not "practical"!
Incredibly practical introduction.......2006-10-30
This book is perfect if you are trying to get your hands around what data mining and machine learning is. Most of the books I have read on this subject want to start with equations and get more complex from there, with little practicality. This book makes extensive use of examples and introduces the mathematical basis for algorithms where needed. The authors make the point that simpler algoritms often work best for solving machine learning problems. Similarly, I would argue, simpler books work best for understanding highly complex fields. I very highly recommend this book.
Book Description
This book describes in detail many of the AI techniques used in modern computer games, explicity shows how to implement these practical techniques within the framework of several game developers with a practical foundation to game AI.
Customer Reviews:
Misleading Title.......2007-09-13
Misleading Title
This book has a misleading title. I think this book should be called "Fundamentals of Game Development" instead of Programming Game AI by Example. Sure the book delivers on what it promises, as so many reviewers have already mentioned, but I believe that his book teaches you something that no other book that at least I am aware of teaches - good software practices coupled with actual game development.
There are dozens of books available in the market today claiming to teach you Game Architecture. I will not name the exact titles of such books but you know what I am talking about. I have read so many such books only to find myself becoming a graphics programmer. Not that I didn't want to become a graphics programmer, but even after learning all that stuff, there was "something" missing. That something turns out to be design patterns related to game development. In other words, how to combine various game components together into a working game. The last place I expected to learn it in such an elegant and easy to understand manner was in an AI book.
It's my sincere belief that this is the best book on game development available in the market. Read that again, not just AI but Game Development. Thats right, no matter which of the numerous disciplines of game development you wish to specialize in, no matter which platform or technologies you want to develop for, you need this book. Period.
The book starts off with the most relevant mathematics chapter that I have found in any game development book. Maybe I'm stupid, but this was the only book that explained to me what a radian was! The lesson on vectors presented many useful examples that are bread and butter of game development but for some reasons are ignored by mainstream game development books. For example, finding out whether a game agent is in front of the player of behind him.
Next, it teaches you the "state design pattern". To me this chapter alone is the entire cost of the book itself. I had never seen this discussed in any game development books. I believe this should be in the appendix of each book that claims to teach game development. Instead, almost all entry level game development books have a C++ primer, but never a primer on topics like the state design pattern or UML. In one appendix this book with teach you all the UML you need to get started. Did I mention the "telegram" pattern? Again, so many "beautiful" books claiming to teach you game architecture only end up teaching you graphics programming and using some API in general. This is the first book I have come across that taught this design pattern. Strange, given the fact that you cannot make a game without a robust HSM and some way of sending messages.
Then it goes on to teach you how to create autonomous game agents. Whether you are into AI or not, this is something you need to know. But what I appreciated most about this chapter was the application of state machines and how physics is implemented in a game at an architectural level. Most books uses procedural approach when to comes to actual implementation. This book uses good object oriented techniques to show you how forces on an agent are accumulated and how it gets translated to the agent's movement.
While I was still amazed how much money I wasted trying to learn how things are put together, this book surprised me with a complete mini soccer game! Now, I learned more about game development from this single chapter than I had from reading complete so called game programming books.
Each and every chapter in this book is a gem. If you are new to game development, then this book will provide you with the right direction to begin your career. If you already have read so many game programming books in the hope of making an actual game but were never able to do so then this book will seal the deal.
To cut a long story short, the book also delves into graph theory, scripting and other material topics such as fuzzy logic that you can easily read from the index of the author's website.
Chapter 7 of this book teaches you how to create the AI of a FPS. Rest assured, you will end up learning more from this chapter than you initially sighed for. And you wont be disappointed. One way to think of this chapter is not an AI simulation, but the framework of an entire FPS game! Thats right, it contains everything except the graphics engine, sound engine etc. You can plug a 3D engine with it, along with other missing libraries and get a 3D game! Other books take a complete opposite approach. They go into great details as to how to create various game libraries and in the end slap you with a pathetic design to hang them on to. After reading this book, all the other game programming books will make sense.
The only qualm I have with this book is that the author has not upgraded the scripting code on his web site. This book teaches you how to integrate Lua into your game engine. Since the publication of this book, Lua has been changed dramatically. Getting Lua, Luabind and Boost to work together was a nightmare, which I am still not able to do successfully. So now I am integrating python into my game. This is when you try to integrate their latest versions. They will work if you stick to the older ones though. But this does not render this chapter useless as you will still learn a lot from it.
If you are beginning game programming, or have given it up in frustration, this book will get you back in the game! If I am asked to name one game development book that I want to suggest to a newbie (who knows C++), this is it. Pick it up with your eyes closed. You wont regret it.
Wish i could spend more time on it..........2007-08-15
Likes:
- How the author only deals with AI game programming and doesn't put in a lot of fluff
- The broad range of topics the book covers
- The use of actual 2D examples and an actual game "Raven"
Dislikes:
- Use of "helper" files that have no explanation in the book (some are explained in previous chapters but the author should have included an appendix to list and explain all the files in the common directory)
- The code explanation is shallow when you consider the fact that the author neglects to tell you about the, many and crucial, other files that are needed to run the program.
- The actual code that can be downloaded from the web site needs a lot of tweaking before it even works (you should just be able to unzip it and run it).
- Chapter 3 and how the author only includes the source code for a final all encompassing program instead of smaller easier to understand projects.
- If you don't know Win32GDI then learn because the book uses it extensively to output to the screen, and that can interfere with understanding the actual meat of the program.
Summary:
If you are going to buy this book make sure you have a lot of time on your hands to look through the source code, tweak it, and pull it apart. The book itself just doesn't give enough explanation to allow a person to create their own version of the concepts without digging through the source code. Overall I would recommend the book to people with an intermediate knowledge of C++ and have very good 2D math skills. This book is a fine overview of major topics in game AI but is sorely lacking (add another 100 pgs of quality explanation on topics). It would probably be necessary to buy other books that are more specific in their focus.
Immensely Useful.......2007-06-13
This book is simply outstanding. The material is crystal clear, direct to the point, and so easy to understand that my 10 year old son is writing state machines in Visual Basic. Although the code is C++ I had no trouble translating it to Objective-C for my use. I am quite impressed at how the author makes such high-level concepts so easy to understand. A must have for every game programmer.
Best book about AI around..........2007-04-24
If you are like me, who wants to get the job done, this book is just perfect!
With lots of examples, this book is stuffed with clever solutions for pratical problems you stumble upon when developing average games. There is even a simple soccer game example.
After you read this book, I'm pretty sure you will obtain solid notions about artificial intelligence for games.
It saved my life.......2007-03-25
I had a problem programming a NPC car pilot and I found the solution inside this amazing book! I'm sure you'll find the solution for your problem as well!!
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- Debunking 9/11 Myths: Why Conspiracy Theories Can't Stand Up to the Facts
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