Introduction To Statistical Pattern Recognition (Computer Science and Scientific Computing Series)

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.

An Introduction To Support Vector Machines: And Other Kernel-Based Learning Methods

Average customer rating:

More for mathematicians than computer scientist
A little dry.
Not even close to an intro...
Excellent book
This is it !

An Introduction To Support Vector Machines: And Other Kernel-Based Learning Methods
Nello Cristianini
Manufacturer: Cambridge University Press
ProductGroup: Book
Binding: Hardcover

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Similar Items:

Kernel Methods for Pattern Analysis
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)
The Elements of Statistical Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Classification (2nd Edition)

ASIN: 0521780195

Book Description

This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software make it an ideal starting point for further study.

Customer Reviews:

More for mathematicians than computer scientist.......2006-09-20

This book introduces the concepts of kernel-based methods and focuses specifically on Support Vector Machines (SVM). It is hard to read and a good background in mathematic is clearly needed. The book has a strong emphasis on SVM starting from the very first line of text. Concepts are well explained, although equations are not clear. The notation doesn't facilitate the reading at all. The book covers linear as well as kernel learning. The kernel trick is well described. It is easy to understand ideas behind SVM while reading the corresponding chapter. Finally a small chapter on SVM applications is proposed. Unfortunately, it only contains typical SVM applications (i.e. standard problems).

I think this book is good if you:

* Have a strong mathematical background
* Work in the specific domain of SVM (or kernel-based methods in general)
* Want to write a research paper about SVM and need the correct notations

However, this book is NOT intended for people who:

* Don't like to read theorems, corollaries and remarks
* Are not interested in reading hundreds of proofs

This is my personal opinion as a computer scientist: this book is definitely written for mathematicians.

A little dry........2006-01-09

The book is a little dry at times. Also, I didn't get a very clear idea of how to select kernel functions, which seems pretty important.

Not even close to an intro..........2004-03-21

Oh Puhleeeezzzzz... How is your vector math??? Remember your linear algebra well? Do you have a background in SVM's? Intuitively able to suck out of thin air the meaning of the Gamma co-efficient as applied to svm's?? You've read all the background papers and remember your formal logic???? No?? too bad..your out of luck..

This book is more aptly titled an Introduction to the Formalisms of SVM's. If your a software engineer trying to implement one of these, forget it.. Be nice if they put that quadratic algorthim psuedocode into something more readable than greek symbology..

If you are trying to build one of these engines, then this book is of absolutely no help, unless you have a background in machine learning and have read all the papers on SVM's. If you can decompose the math into code in your head, then you might find it entertaining... What I don't get is how all the rest of these reviewers can give such "glowing praise" for this book and have it be so completely worthless as an introduction... makes me think some of these are shills..

Bottom line is, if your trying to code a svm, this book will not help. If your trying to understand how to implement a svm, this book will not help. If you are trying to understand how an svm works, this book will not help. If you want to know the mathematical basis for SVM's and like that presentation.. this is the book for you..

Excellent book.......2003-11-19

I just happened to read the reviews on the book on Support vector machines by Nello Cristianini and John Shawe-Taylor. Could not resist adding my own comments about the book. Excellent book. I plan to use the book for the course on "Fundamentals of computer aided engineering" that I teach at the Swiss Federal Institute of Technology, Lausanne (EPFL).

This is it !.......2001-08-31

The book is just great. The appendix on algorithms could have more explanations. Also the application section is a short. It would have been more usuful to take one of these applicaitons and describe it in details. But all in all, the book is excellent.

An Introduction to Genetic Algorithms (Complex Adaptive Systems)

Average customer rating:

Good Theoretical GA Textbook
Not for beginners
An introduction and much more
A Great Introduction to Genetic Algorithms
Good introduction for such a short book

An Introduction to Genetic Algorithms (Complex Adaptive Systems)
Melanie Mitchell
Manufacturer: The MIT Press
ProductGroup: Book
Binding: Paperback

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Genetic Algorithms in Search, Optimization, and Machine Learning
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence
Introduction to Evolutionary Computing (Natural Computing Series)
How to Solve It: Modern Heuristics
Genetic Programming: On the Programming of Computers by Means of Natural Selection (Complex Adaptive Systems)

ASIN: 0262631857

Book Description

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics.

Customer Reviews:

Good Theoretical GA Textbook.......2005-05-06

This book primarily deals with the theoretical side of genetic algorithms. If you are looking for practical knowledge of how to implement a GA you should look elsewhere. For all intents and purposes this is a textbook. It's heavy on theory and proofs, but doesn't always explain everything in depth (that's what class time is for). There are problems at the end of each chapter that can be assigned to students.

There are case studies of many academic projects that seem to drone on forever and aren't really that useful in helping you learn how to write your own GA. Chapter 1 gives an overview and provides all of the appropriate terminology. Chapter 5 gives an high-level overview of how to implement a GA. Those are the 2 must-read chapters, all of the others can be used as torture for CS students.

To recap, if you're teaching a class in artificial intelligence this book is good. If you're trying to figure out how to implement a GA to solve a practical problem not so good. That evens out to 3 stars for my rating. I recommend searching the web, there are a few good sites on GA programming.

Not for beginners.......2004-02-04

I have an engineering degree, and I found this to be a little tough to follow for two reasons:

1. Not enough step by step prodecure especially at the beginning. Mitchell is too quick to start with the math formulas. It turns out that Genetic Algorithms are fairly straight forward and easy to follow, but you have to read this book twice before you "get it" because Mitchell clouds the discussion with proofs and mathematical representations of systems. It is tough to follow.

2. Mitchell does a poor job of selecting meaningful examples to illustrate the points. A nice simple set of examples where the average person easily picture the system would have been delightful. Instead this author chooses to illustrate the Genetic Algorithms through uncommon neural networks amoung other exotic applications. I found myself struggling to understand both the example (I didn't know a thing about neural networks!) and the genetic algorithm.

When buying an Introduction type book, I expected it to be more 'down to earth'. this book is for advanced minds!

An introduction and much more.......2004-01-26

First it must be said that the book is not an introduction that the non-scientist will easily understand. Some knowledge of computer programming is assumed. It acknowledges this in the last paragraph of the preface. Many of the notations in the book are unfamiliar to business or financial readers. There is no mathematics beyond algebra so the aforementioned prerequisites are the main hills to climb.

Mitchell's book is an overview of genetic algorithm analysis techniques as of 1996. The author gives a history of pre-computer evolutionary strategies and a summary of John Holland's pioneering work. A description of the basic terminology is presented and examples of problems solved using a GA (such as the prisoner's dilemma). The second chapter discusses evolving programs in Lisp and cellular automata. Also included in this chapter is a discussion of predicting dynamical systems. This was the section that has the most interest for me. Also interesting was the summary in this chapter about putting GAs into a neural network so that the ANNs could evolve.

The fifth chapter discusses when to employ a GA for maximum success. I appreciate the clearly thought out discussion of when to choose a GA for a problem. Sometimes authors of these types of books mimic the man with a hammer that thinks everything looks like a nail.

A Great Introduction to Genetic Algorithms.......2002-12-07

This is a great place to start to learn about genetic algorithms. The writing is clear and not bogged down by jargon. The book is not overly technical; it is written for the layman and has a casual conversational style that is a pleasure to read.

About half of the book is devoted to presenting examples of studies that have used genetic algorithms. These examples are interesting in themselves and also serve to illustrate the variety of genetic approaches that are available. The book also presents conflicting points of view of experts about which algorithms work best and why. This is helpful in combatting the impression that a beginner sometimes gets that everything is simple and all the answers are known.

Good introduction for such a short book.......2002-04-07

Although short, this book gives a good introduction to genetic algorithms for those who are first entering the field and are looking for insight into the underlying mechanisms behind them. It was first published in 1995, and considerable work has been done in genetic algorithms since then, but it could still serve as an adequate introduction. Emphasizing the scientific and machine learning applications of genetic algorithms instead of applications to optimization and engineering, the book could serve well in an actual course on adaptive algorithms. The author includes excellent problem sets at the end of each chapter, these being divided up into "thought exercises" and "computer exercises", and in the latter she includes some challenge problems for the ambitious reader.

Chapter 1 is an overview of the main properties of genetic algorithms, along with a brief discussion of their history. The role of fitness landscapes and fitness functions is clearly outlined, and the author defines genetic algorithms as methods for searching fitness landscapes for highly fit strings. An elementary example of a genetic algorithm is given, and the author compares genetic algorithms with more traditional search methods. The author emphasizes the unique features of genetic algorithms that distinguish them from other search algorithms, namely the roles of parallel population-based search with stochastic selection of individuals, and crossover and mutation. A list of applications is given, and two explicit examples of applications are given that deal with the Prisoner's Dilemna and sorting networks. The author also gives a brief discussion as to how genetic algorithms work from a more mathematical standpoint, emphasizing the role of Holland schemas. The reader more prepared in mathematics can consult the references for more in-depth discussion.

The next chapter stresses the role of genetic algorithms in problem solving, beginning with a discussion of genetic programming. Automatic programming has long been a goal of computer scientists, and the author discusses the role of genetic programming in this area, particularly the work of John Koza on evolving LISP programs. In addition, she discusses the current work on evolving cellular automata and its role in automatic programming. The latter discussion is more detailed, this resulting from the author's personal involvement in artificial life research. Those interested in time series prediction tools will appreciate the discussion on the use of genetic algorithms to predict the behavior of dynamical systems, with an example given on predicting the behavior of the (chaotic) Mackey-Glass dynamical system. The author also gives applications of genetic algorithms in predicting protein structure, an area of application that has exploded in recent years, due to the importance of the proteome projects. The area of neural networks has also been influenced by genetic algorithms, and the author discusses how they have replaced the familiar back-propagation algorithm as a method to find the optimal weights.

Chapter 3 is more in line with what the author intended in the book, namely a discussion of the relevance of genetic algorithms to study the mechanisms behind natural selection. She discusses the "Baldwin effect", which gives a connection between what an organism has learned (a small time-scale process) to the evolutionary history of the Earth (a long time-scale process). A simple model of the Baldwin effect is given using a genetic algorithm, along with a discussion of the Ackley-Littman evolutionary reinforcement learning model, which involves the use of neural networks, and which is another computational demonstration of the Baldwin effect. In addition, the author discusses models for sexual selection and ecosystems based on genetic algorithms. These are the "artificial life" models that the author has been involved in, and she gives a very understandable overview of their properties.

Chapter 4 should suit the curiosity of the mathematician or computer scientist who wants to understand the theoretical justification behind the use of genetic algorithms. Again employing the Holland notion of schemas and adaptation as a "tension between exploration and exploitation", the author formulates a mathematical model, called the Two-Armed Bandit Problem, of how genetic algorithms are used to study the tradeoffs in this tension. The level of mathematics used here is very elementary with the emphasis placed on the intuition behind this model, with only a sketch of the model's solution given. To address the role of crossover in genetic algorithms, the author discusses in detail a class of fitness landscapes, called "Royal Road functions" that she and others have developed. The performance of the genetic algorithm employed is then compared against the three different hill-climbing methods. Formal mathematical models of genetic algorithms are also discussed, one of which involves dynamical systems, another using Markov chains, and one using the tools of statistical mechanics. The latter is very interesting from a physics standpoint but is only briefly sketched. The interested physicist reader can consult the references given by the author for further details.

Practical use of genetic algorithms demands an understanding of how to implement them, and the author does so in the last chapter of the book. She outlines some ideas on just when genetic algorithms should be used, and this is useful since a newcomer to the field may be tempted to view a genetic algorithm as merely a fancy Monte Carlo simulation. The most difficult part of using a genetic algorithm is how to encode the population, and the author discusses various ways to do this. She also details various "exotic" approaches to improving the performance of genetic algorithms, such as the "messy" genetic algorithms. One must also choose a selection method when employing genetic algorithms, and the author shows how to do this using various techniques, such as roulette wheel and stochastic universal sampling. In addition, genetic operators must also be chosen in implementing genetic algorithms, and the author emphasizes crossover and mutation for this purpose. Lastly, the values of the parameters of the genetic algorithm, such as population size, crossover rate, and mutation rate must be chosen. The author discusses various approaches to this. Although brief, she does give a large set of references for further reading.

Average customer rating:

Clear and logical exposition

Introduction to the Theory of Neural Computation

A Broad Survey

Introduction to the Theory of Neural Computation (Santa Fe Institute Studies in the Sciences of Complexity)
John A. Hertz , Richard G. Palmer , and Anders Krogh
Manufacturer: Westview Press
ProductGroup: Book
Binding: Paperback

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Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems

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Neural Networks For Pattern Recognition

Information Theory, Inference & Learning Algorithms

ASIN: 0201515601

Amazon.com

This book comprehensively discusses the neural network models from a statistical mechanics perspective. It starts with one of the most influential developments in the theory of neural networks: Hopfield's analysis of networks with symmetric connections using the spin system approach and using the notion of an energy function from physics. Introduction to the Theory of Neural Computation uses these powerful tools to analyze neural networks as associative memory stores and solvers of optimization problems. A detailed analysis of multi-layer networks and recurrent networks follow. The book ends with chapters on unsupervised learning and a formal treatment of the relationship between statistical mechanics and neural networks. Little information is provided about applications and implementations, and the treatment of the material reflects the background of the authors as physicists. However the book is essential for a solid understanding of the computational potential of neural networks. Introduction to the Theory of Neural Computation assumes that the reader is familiar with undergraduate level mathematics, but does not have any background in physics. All of the necessary tools are introduced in the book.

Customer Reviews:

Clear and logical exposition.......2007-08-18

It's not the latest book on this topic, so today, there are other texts that have more recent developments to be sure. I originally read this text about 15 years ago. But what I got from this book, that I didn't get from most, are important insights and clear understanding of the material that's covered. The authors have a deep understanding, and have teaching as their goal in writing. Most other texts in this area are lacking in one or both of those characteristics, and aren't worth the paper they are printed on.

Introduction to the Theory of Neural Computation.......2000-10-06

This book is written from a mathematical perspective. The book introduces the Hopfield Neural Network with history and applications. The authors solve the network problem and develop the Hebb Rule. Links are made to Ising Spin models and stochastic problems. I find this book to be one of the best written mathematical guides for Neural Networks.

A Broad Survey.......1997-11-08

This was a good survey, and well-grounded mathematically. It is kind of scattershot, and if you primarily want to do practical projects like predicting financial markets, a lot of the sections won't be relevant. But if you want a broad-based approach, emphasizing a variety of network designs fro different purposes, this book is very good.

Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition

Average customer rating:

Needs a second volume which explains the first
I looked for
The a good introduction to NLP, but could be improved
Good oveview, slightly overrated: broad and shallow
Good, but many errors

Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition
Daniel Jurafsky , and James H. Martin
Manufacturer: Prentice Hall
ProductGroup: Book
Binding: Paperback

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Similar Items:

Foundations of Statistical Natural Language Programming
The Oxford Handbook of Computational Linguistics (Oxford Handbooks in Linguistics)
Spoken Language Processing: A Guide to Theory, Algorithm and System Development
Mining the Web: Discovering Knowledge from Hypertext Data
Natural Language Understanding (2nd Edition)

ASIN: 0130950696

Book Description

This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora. Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.

Customer Reviews:

Needs a second volume which explains the first.......2005-05-20

This book is by now an accepted classic in the field. It is basically the only textbook that covers so much of computational linguistics, so I have had no choice but to use it for the past several years. Just the same, I'd rather not use it for teaching linguistics students. While the book has much to offer the professional, including a broad range of topics extensively researched, it is much more useful in this "handbook" capacity than as a textbook for the uninitiated. The chief reasons for this are: 1) It is pedagogically very poor; the majority of concepts are either explained in a confusing and obfuscatory manner or are not explained and are simply left in algorithmic form. This is not usually edifying to the linguistics student with no computer science background. 2) There are too many mistakes in its algorithms and method overviews. So far as I can see, even the famed Earley parsing algorithm is wrong here, it will not yield the correct output. 3) It is not written in a language that linguistics students can understand. With no background in mathematics, computer science, or pseudocode, such students need much more coddling than is provided by this book, and they are virtually unable to read it. Basically, as the title to this review states, what is called for now is a book to explain the contents of this book. Perhaps if my students keep encouraging me to write it. . .

I looked for.......2003-11-06

something which I can use - I am a linguist - and found it immensly readable and useful

The a good introduction to NLP, but could be improved.......2003-04-16

This book helped me accomplish what I set out to do; namely to obtain an overview of the field of natural language processing, with an emphasis on language understanding (as opposed to recognition). And I can recommend it on that level. The weakness of the book however is that it left me asking, "OK, now what?". The book started off strong with a number of dynamic-programming algorithms, finite automaton models, and N-grams that one could sink his/her teeth into from an algorithmic point-of-view. But when it came to actual techniques for natural-language understanding (chapters 14-17) the goods were not delivered. The algorithms disappeared, and the best I could find was in Chapter 15 an incomplete, and unconvincing treatment of Hiyan Alshawi's semantic parsing techniques which fueled the Core Language Engine last decade. Chapter 16 dealt with lexical semantics and was almost entirely devoid of algorithms.

My gut feeling after reading this text is that parsing techniques will likely give way to statistical and probabilistic learning methods that will in some sense bypass the need to correctly or accurately parse language. I cannot fault the authors for not exploring this in more depth,as this represents the cutting edge for both NLP and artificial intelligence. In any case, I'm off to read Schutze and Manning's book which will hopefully provide a bit more focus on that perspective. What intrigues me is that most people can understand some language, but very few people understand the grammar of their own language, especially if they have been deprived of a formal education. So why should computers need to know all about grammar rules and parsing? Could they instead be trained by simply being exposed to enough interactions between language and objects? I teach in a department dominated by both foreign and immigrant students. I understand them most of the time, but I would estimate that half the time their sentences or utterances would not fail to be parsed correctly.

Good oveview, slightly overrated: broad and shallow.......2002-05-26

GENERAL IDEA: Broad coverage, it lacks depth and details - particularly practical details. That is, the presentation is often sketchy, mainly because it approaches too many subjects for its available space. I would not say that this book is strong on theory either. It is quite obvious that it avoids getting too formal and precise, probably to remain attractive for non-specialists too.

CASE STUDY: One specific problem I had with the Hidden Markov Models, that are supperficially presented (or spread I could say) in several separate sections of the book, so it's not been a pleasure trying to actually understand them properly and completely as a fundamental concept, to make them work in my particular application.

TITLE: The book's title IS misleading because it starts with "Speeech" and this book's main subject is not speech but (written) language. Actually there are only a few chapters on speech.

CONCLUSION: Get this book if you are looking for a good overview of the field. The book will introduce you to a thousand of topics. As soon as you need in-depth coverage of some particular topic, you will look for additional resources.

Good, but many errors.......2002-05-20

This book is a great general introduction to NLP, covering a broad range of topics. Unfortunately there are many errors in the mathematical formulae and the algorithm descriptions, so do make sure to download the errata list from the book's home page.

Introduction to Automata Theory, Languages, and Computation (2nd Edition)

Average customer rating:

A Butchered Classic
Updated Classic Text
Good, but just it
Automata theory. The heart of Computer Science
Eh... Whatever...

Introduction to Automata Theory, Languages, and Computation (2nd Edition)
John E. Hopcroft , Rajeev Motwani , and Jeffrey D. Ullman
Manufacturer: Addison Wesley
ProductGroup: Book
Binding: Hardcover

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ASIN: 0201441241

Amazon.com

This book is a rigorous exposition of formal languages and models of computation, with an introduction to computational complexity. The authors present the theory in a concise and straightforward manner, with an eye out for the practical applications. Exercises at the end of each chapter, including some that have been solved, help readers confirm and enhance their understanding of the material. This book is appropriate for upper-level computer science undergraduates who are comfortable with mathematical arguments.

Customer Reviews:

A Butchered Classic.......2007-09-28

I've heard that the first edition of this book is a classic. Reading the second edition, I can kind of see that -- occasionally there will be a stretch of 5 pages or so that is wonderfully clear, concise, and informative.

But overall, this edition is a disappointment. The explanations tend to be mechanical and unhelpful, and are sometimes confused or just incorrect. New sections on mathematical foundations and applications have been added, but there isn't really adequate space devoted to covering either topic, and the results are so rushed and lacking in context that I can't see those sections being useful to anyone who would need them in the first place. Finally, this edition needs to be proofread for correctness! It contains numerous mistakes, some of them in the presentations of key proofs.

Updated Classic Text.......2007-08-29

The previous edition of this text was published in the late 70's (1979), and it was still in use today in many schools and Universities across the world. For good reason too, the authors of this text really nail down the concept of computability as we understand it today. It is very difficult to find an undergraduate curriculum that does not include a course in Computability or theory of computation, and that is certainly a change from a couple of decades ago where this type of study was left to the Graduate level curricula. What this means to the reader is that one can not be a Computer Scientist without understanding the concepts and theory behind what computability really means.

Things like Context Free languages and grammar are used readily in things like XML and its accompanying standards such as the DTD. So, it makes sense to update a classic text to include such topics and further illustrate to the reader that what once was a theory is now center stage of Computer Science and the IT industry as a whole.

The text starts with the classics such as an introduction to automata theory followed by languages. The authors have taken a more relaxed approach to the topics as the proofs are less formal and easier to follow. Plain text is usually used to informally proof the topic at hand, and the authors go into a more formal approach on selected proofs. This is definitely a better approach than the other texts in the same topic that proofs are center stage of the discussion and the reader gets lost early on in the process. The text is easy to read for students, and easy to explain for the instructors. I remember when I took theory of Computation for my graduate work proofs were so convoluted and difficult to read that I had to spend many of nights trying to understand what the instructor was talking about in the class.

As one would expect, the book then goes into Turning Theory and Machine with the concept to computability and complexity. Well, the good news is that the authors' approach to the topic does not change; lots of explaining of the basics followed by a more detailed formal approach to the topic. All I need to say is that I wish my text was this reader friendly! Chapter 8, Introduction to Turing Machines, sets the ground work for the rest of the text. It explains reducibility and more importantly how to reduce a problem, something I have never seen in any other text in such detail! Automata and its relation to Turing Machine is depicted in detail, so there is no gap between the topics. What is interesting is that the authors close the loop with actually talking about, for example the Halting problem, in the real world with a program.

As one would expect, different classes of problems are explored in detail with many examples (theory and real-world examples) that accompany the topic at hand. Each chapter ends with a summary of topics discussed followed by a set of exercises. There are also a number of exercises at the end of each section in a given chapter in order to reel-in the topic for the reader.

All and all, this is one great text on automata and computation theory. It is easy to read and follow for the students without the loss of content. The authors relate abstract concepts to real-world examples to further illustrate the importance of the topic at hand.

Good, but just it.......2007-06-27

A good book, but just it.
It's like a normal book. It's not bad but not excellent...

Automata theory. The heart of Computer Science.......2007-04-06

Excellent book. Nothing to say for this one.

Eh... Whatever..........2007-01-21

Uhm... I had to buy this book because it was a required text for a required course. Who would buy a book like this otherwise? Duh!

Life: An Introduction to Complex Systems Biology (Understanding Complex Systems)

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Life: An Introduction to Complex Systems Biology (Understanding Complex Systems)
Kunihiko Kaneko
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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Similar Items:

Introduction to Systems Biology: Design Principles of Biological Circuits (C&H/CRC Mathematical & Computational Biology Series)
Systems Biology: Properties of reconstructed Networks
Evolutionary Dynamics: Exploring the Equations of Life
Structure and Dynamics of Networks (Princeton Studies in Complexit)
The Emergence of Life: From Chemical Origins to Synthetic Biology

Accessories:

Intermediate Physics for Medicine and Biology (Biological and Medical Physics, Biomedical Engineering)
Controlled Nanoscale Motion: Nobel Symposium 131 (Lecture Notes in Physics)
Physics of the Human Body: A Physical View of Physiology (Biological and Medical Physics, Biomedical Engineering)

ASIN: 3540326669

Book Description

What is life? Has molecular biology given us a satisfactory answer to this question? And if not, why, and how to carry on from there? This book examines life not from the reductionist point of view, but rather asks the question: what are the universal properties of living systems and how can one construct from there a phenomenological theory of life that leads naturally to complex processes such as reproductive cellular systems, evolution and differentiation? The presentation has been deliberately kept fairly non-technical so as to address a broad spectrum of students and researchers from the natural sciences and informatics.

Introduction to Robotics: Analysis, Systems, Applications

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Good and not so good (bad?)

Introduction to Robotics: Analysis, Systems, Applications
Saeed B. Niku , and Saeed B Niku
Manufacturer: Prentice Hall
ProductGroup: Book
Binding: Hardcover

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Similar Items:

Modelling and Control of Robot Manipulators (Advanced Textbooks in Control and Signal Processing)
Nuclear Energy: An Introduction to the Concepts, Systems, and Applications of Nuclear Processes
Introduction to Robotics: Mechanics and Control (3rd Edition)
Robotic Explorations: A Hands-On Introduction to Engineering
Robot Modeling and Control

ASIN: 0130613096

Customer Reviews:

Good and not so good (bad?).......2005-09-02

It starts off well. The sections on kinematics and motion are okay, and even the dynamic analysis is not bad, though a bit light for electrical engineers. Then it takes a turn for the worse and gives a generalized picture of sensors and actuators and vision systems, which is okay if it has never been seen before, but for electrical engineers it is not needed. The section on fuzzy logic is interesting but it is dissapointing that there is not a section on real control systems. I hope that fuzzy logic is not a substitute for convention control as this book seems to imply. Paul's book has much more substance dispite its age. Still, for a survey of the topic or for the computer scientist, maybe it is not that bad of a text.

Average customer rating:

Good intro but dated
One of the best books I recently read!
Good source of Intuition for Information Theory
Good book for the basics of information theory
Layman's Introduction to Information Theory

An Introduction to Information Theory
John R. Pierce
Manufacturer: Dover Publications
ProductGroup: Book
Binding: Paperback

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The Mathematical Theory of Communication
An Introduction to Information Theory
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Mathematical Foundations of Information Theory
Information Theory

ASIN: 0486240614

Book Description

Covers encoding and binary digits, entropy, language and meaning, efficient encoding and the noisy channel, and explores ways in which information theory relates to physics, cybernetics, psychology, and art. "Uncommonly good...the most satisfying discussion to be found." — Scientific American. 1980 edition.

Customer Reviews:

Good intro but dated.......2007-08-08

The update of this book should have been updated. While it is understandable that at the time of the first print of this book in 1961 the author saw little or no practical use for Shannon's information theory (other than perhaps his channel capacity theorem) it was well known by the second printing in 1980 that it has profound implications in studying biology (and modern technology). For instance in an article published in Nature in 1967, A. L. MacKay showed how the genetic code is highly optimal using Huffman's algorithm. More recently Ardell and Sella (with summaries available on the net) have 'demonstrated that the code's present structure was also shaped by natural selection (though non-Darwinian, see below). In this process, the codons - the triplets of nucleotides that map a particular nucleic acid sequence into proteins - are arranged to minimize the negative effects of genetic error, and to optimize the process of 'readout' of genes during protein synthesis. By permuting all 20 amino acids across all possible codon sets, both groups found that the 'universal' genetic code - the one found in nearly every organism on earth...-falls in the best .0001% of all possible codes and perhaps even better, in terms of its capacity to be an error-correcting code...' By showing modifications are possible in one generation the evidence points away from Crick's thesis of the genetic code being a 'fozen accident' but instead possible Lamarckian beginnings with horizantal gene transfer leading to Carl Woese's early RNA World hypothesis before Darwinian vertical descent begins.

The author also tends to perpetuate the widespread misunderstanding (generally by physicists who tend to contort the meaning away from Shannon's into 'available' states or choices such as with Black Holes) that information is uncertainty; he confuses (readers potentially with) surprise versus information by not taking into account the other half of the necessary equation for information transmission, being noise. He says "The amount of information conveyed by the message increases as the amount of uncertainty as to what message actually will be produced becomes greater." [pg 23] While he clears this up in a later chapter on noise it becomes so technical that it appears most readers of Shannon's theory have been mislead. At this point the scientists (usually physicists who actually work with a different concept of 'available information') typically equate the uncertainty with Kolmogorov complexity and assume that maximum information and complexity is randomess.

For instance consider Philip Nelson's comment in his book Biological Physics that 'random messages carry the most information!' In one footnote of his nearly 600 page book he effectively dismisses all of Nobel Prize winner Shannon's information achievements.

Much of the trouble is with terminology. We think of noise as impure sound. Shannon tried to avoid this problem by introducing the term 'equivocation' but on the other hand this seems to have no intuitive meaning in this context. One really has to go to the math to sort it out. The critical equation to potentially eradicate the confusion does not appear in the book -
R = Hbefore - Hafter
H is an entropy-like formula without Boltzman's constant; however the concepts are very different. (Reportedly Von Neuman told Shannon in the 1940's to call his uncertainty 'entropy, as noone will know what you mean!' Apparently this is still working!) Entropy of the universe apparently increases under the 2nd law of thermodynamics (at least ignoring gravity and extensivity), information begins and ends with life (one needs a recognizer to measure it). A random message in fact carries no information as there is no resolution (reduction) of uncertainty. This is all explained at molecular biologist's Dr. Tom Schneider's website, I know of no other comprehensive source and certainly no book that gets it right. (As yet! 'Hope springs eternal!' A. Pope; 1688 - 1744)

One of the best books I recently read!.......2007-01-11

A very good introductory text to information theory. Written in a plain, comprehensive way without too many unnecessary equations. I recommend this book to anyone looking for a book in such topic!

Good source of Intuition for Information Theory.......2006-12-26

This book seeks to explain information theory to the layman, and in that regard the author has done a brilliant job. The author explains the implications of the mathematics, without drilling into the gory details in a very appealing fashion. I also appreciated how he explores the limitations of Shannon's results by looking at some related fields.

All in all well worth the time. Although Shannon's work is quite readable by an educated layman, (1-2 years of calculus would be sufficient), it does not provide much context. (No fault of Shannon, that wasn't his goal).

Pierce provides this perspective, and his book is worth a read, even for people who munch theorems along with their cereal for breakfast.

Good book for the basics of information theory.......2005-12-08

I give this book five stars because it succeeds brilliantly at what it sets out to do - to introduce the field of information theory in an accessible non-mathematical way to the completely uninitiated. Information theory is that branch of mathematics that deals with the information content of messages. The theory addresses two aspects of communication: "How can we define and measure information?" and "What is the maximum information that can be sent through a communications channel?". No other book I know of can explain these concepts of information, bits, entropy, and data encoding without getting bogged down in proofs and mathematics. The book even manages to equate the concept of language with the information it inherently transmits in a conversational and accessible style. The book rounds out its discussion with chapters on information theory from the perspectives of physics, psychology, and art. The only math necessary to understand what's going on in this book is high school algebra and the concept of logarithms. If you are an engineer or engineering student who knows anything about information theory, you probably will not find this book helpful. Instead you would do better to start off with a more advanced book like "An Introduction To Information Theory" by Reza, which introduces concepts from a more mathematical perspective.

Layman's Introduction to Information Theory.......2005-05-15

This is a good introduction to the concepts of information theory: entropy, stationarity, ergodic sources, efficient coding, error detection, error correction and geometrical modelling. It is aimed at the layman, so there is plenty of explanatory text for each equation presented. If you know basic algebra, probability and logarithms (especially logarithms of base 2), you would get more out this book.

There should have been a more detailed explanation of how signals are transmitted and received; the difficulty of receiving a signal is described in Chapter II and there is a mention of FM transmission in Chapter IX. While the actual transmission and reception of signals may not be part of information theory, including a discussion on the most familiar use of information theory would have made this book more satisfying.

In the second half of the book, Pierce applies information theory to areas other than communications. The chapters relating to physics (X) and art (XIII) are clear, but I found the ones on cybernetics (XI) and psychology (XII) a bit of a strain. For computer-savvy folk, the chapter on cybernetics is very dated but this is no fault of the author since this book was last updated in 1979.

Kam-Hung Soh, 15 May 2005.

Introduction to Neural Networks with Java

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A bit disappointed because I expected more from this book.
Excellent practical book on neural networks using Java
Unique book

Introduction to Neural Networks with Java
Jeff, T Heaton
Manufacturer: Heaton Research, Inc.
ProductGroup: Book
Binding: Paperback

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Neural Networks: A Comprehensive Foundation (2nd Edition)
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Programming Spiders, Bots, and Aggregators in Java

ASIN: 097732060X

Book Description

Introduction to Neural Networks in Java introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures such as the feedforward backpropagation, Hopfield, and Kohonen networks are discussed. Additional AI topics, such as Genetic Algorithms and Simulated Annealing, are also introduced. Practical examples are given for each neural network. Examples include the Traveling Salesman problem, handwriting recognition, fuzzy logic and learning mathematical functions. All Java source code can be downloaded online. In addition to showing the programmer how to construct these neural networks, the book discusses the Java Object Oriented Neural Engine (JOONE). JOONE is a free open source Java neural engine.

Customer Reviews:

A bit disappointed because I expected more from this book........2006-06-19

I have been reading through the book. Actually it provides very clear explanations, but I had the impression the author talks too much and keep saying the same things over and over again. The book could be half its volume with the same content of knowledge. Besides the provided examples are a bit too simple and obvious.
Nothing much to put under the tooth. After reading it I felt left with my hunger for something deeper and more consistent. The algorithms provided also merely implement and stick to the few examples introduced. On the course of the book, the author wanders from the main point which is first and foremost to discuss neural networks under all angles. He unexpectedly brings up Fuzzy logic and Genetic algorithms which is not what the book title purports to talk about: a bit of confusion.
Overall there is a bit of deception, but indeed the book does what its title says : it is really just an "introduction" to Neural Networks with Java and nothing more. I would recommend it to somebody seeking to embrace the field and who is really a beginner in the domain.

Excellent practical book on neural networks using Java.......2006-03-27

Programming Neural Networks in Java will show the intermediate to advanced Java programmer how to create neural networks. This book attempts to teach neural network programming through two mechanisms. First the reader is shown how to create a reusable neural network package that could be used in any Java program. Second, this reusable neural network package is applied to several real world problems that are commonly faced by programmers. This book covers such topics as Kohonen neural networks, multi layer neural networks, training, back propagation, and many other topics. The content of the book is as follows:
Chapter 1: An Introduction to Neural Networks
The structure of neural networks will be briefly introduced in this chapter. Also discussed is the history of neural networks, since it is important to know where neural networks came from, as well as where they are ultimately headed. Finally, there is a broad overview of both the biological and historic context of neural networks.
Chapter 2: Understanding Neural Networks
A neural network can be trained to recognize specific patterns in data. This chapter will teach you the basic layout of a neural network and end by demonstrating the Hopfield neural network, which is one of the simplest forms of neural network.
Chapter 3: Using Multilayer Neural Networks
You will see how to use the feed-forward multilayer neural network and two ways that you can implement such a neural network. The chapter begins by examining an open source neural network engine called JOONE. JOONE contains a neural network editor that allows you to quickly model and test neural networks.
Chapter 4: How a machine learns
Every learning algorithm involves somehow modifying the weight matrices between the neurons. This chapter examines some of the more popular ways of adjusting these weights.
Chapter 5: Understanding Back Propagation
This chapter examines one of the most common neural network architectures-- the feed foreword back propagation neural network.
Chapter 6: Understanding the Kohonen Neural Network
The Kohonen neural network contains no hidden layer. The Kohonen neural network differs from the feedfroward back propagation neural network in several important ways. This chapter examines the Kohonen neural network and how it is implemented.
Chapter 7: Optical Character Recognition
This chapter develops an example program that can be trained to recognize human handwriting. It is not a program that can scan pages of text. Rather this program will read character by character, as the user draws them. This function will be similar to the handwriting recognition used by many PDA's.
Chapter 8: Understanding Genetic Algorithms
A chapter on an AI technology unrelated to neural networks.
Chapter 9: Understanding Simulated Annealing
A second AI technology that can be used to train neural networks.
Chapter 10: Eluding Local Minima
One of the most fundamental flaws is the tendency for the backpropagation training algorithm to fall into a "local minima". A local minimum is a false optimal weight matrix that prevents the backpropagation training algorithm from seeing the true solution. This chapter shows how to use certain training techniques to supplement backpropagation and elude local minima.
Chapter 11: Pruning Neural Networks
This chapter examines several algorithms that modify the structure of the neural network. This structural modification will not generally improve the performance of the neural network, but makes it more efficient. If a particular neuron's connection to other neurons does not significantly affect the output of the neural network, the connection will be pruned.
Chapter 12: Fuzzy Logic
Fuzzy logic is a branch of AI not directly related to the neural networks examined so far. Fuzzy logic is often used to process data before it is fed to a neural network, or to process the outputs from the neural network. Fuzzy logic is examined in reference to removing SPAM from emails.
Appendix A: JOONE Reference
Appendix B: Mathematical Backgrounder
Appendix C: Using the Examples on a Windows System
Appendix D: Using the Examples on a UNIX System
This book is currently available online. Since Amazon throws out reviews with web addresses in them, suffice it to say that you just need to type "HeatonResearch" into Google. The 2nd address is the one you want. This book couples accessible instruction with plenty of code that you can lift to make your own neural network applications. I highly recommend it.

Unique book.......2006-01-31

I have received my copy of the book and I can't put it down. It has been great help with my AI research at the University. I have the other book from the same author "Programming Spiders, Bots and Aggregators in Java" and I have the same comments for both. Both are easy to read, have precise information and great code. Chapter 7 of this book "OCR with Kohonen Neural Network" makes the book more than worth it. Great stuff. I hope the author does not stop and keep writting books like these. I recommend this book for anyone interested in learning AI and also experienced programmers alike. The author makes though topics seem easy. Highly recommended.

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