Book Description
Loads of pictures and very frank discussion make this book a pleasure to read, and a real learning tool.
— Craig Maloney, Slashdot Contributor
The author gives lots of practical advice, some of which would be useful even to experienced tinkerers. It is very thorough.
— Edward Chin, The Canadian Linux Users' Exchange
Learning robotics by yourself isn't easy, but it helps when the encouragement comes from an expert who's spent years in the field. Not only does Author David Cook assist you in understanding the component parts of robot development, but he also presents valuable techniques that prepare you to achieve new discoveries on your own.
Cook begins with the anatomy of a homemade robot and gives you the best advice on how to proceed successfully. General sources for tools and parts are provided in a consolidated list, and specific parts are recommended throughout the book. Also, basic safety precautions and essential measuring and numbering systems are promoted throughout.
Specific tools and parts covered include digital multimeters, motors, wheels, resistors, LEDs, photoresistors, transistors, chips, gears, nut drivers, batteries, and more. Robot Building for Beginners is an inspiring book that provides an essential base of practical knowledge for anyone getting started in amateur robotics.
Download Description
Learning robotics by yourself isn't easy. It helps when the encouragement comes from someone who's been there. Not only does Robot Building for Beginners assist you in understanding component parts of robot development, but also it prepares you with techniques to learn new discoveries on your own.
Author David Cook begins with the anatomy of a homemade robot and gives you the best advice on how to proceed successfully. General sources for tools and parts are provided in a consolidated list, and specific parts are recommended throughout the book. Also, learn basic safety precautions and essential numbering and measuring systems.
An in-depth analysis of digital multimeters gives you all the information you need to select and obtain this valuable tool for yourself. Other tools and parts covered include: motors, wheels, resistors, wire strippers, needle nose pliers, tap and die, alligator clips, LEDs, solderless breadboards, soldering irons, heat-shrink tubing, photoresistors, transistors, chips, gears, nut drivers, screws, cut-off wheels, connectors, and batteries.
Robot Building for Beginners is an inspiring book that provides basic, practical knowledge on getting started in amateur robotics.
Customer Reviews:
Excellent book.......2007-08-01
This is an excellent book for a beginner. I'm a software engineer, didn't have any knowledge about robotics prior to this book, and yesterday I finished building my first robot - Kobi (Jacob, which is a rather biblical name but in hebrew also literally means 'will follow'... as it follows lines of course). Took me about 1-1.5 months to read, gather parts and build.
I live in Israel and didn't want to order all the parts from the web (wanted to get it fast), so I bought what I found in the stores, improvised a little bit (just a bit) and built Kobi.
The book takes you through the electronics, including all the parts and tools to use, and the body building (quite a simple body - adjusting a container). Well written, amusing, very readable, lots of pictures to show the reader how things are supposed to be.
This is my first robotics book. I chose it after reading the excellent reviews on it in Amazon, now I'm adding mine. This book greatly enhanced my knowledge (and practice) in robotics, and I already ordered some more robotics books to learn more.
Beginning Electronics.......2007-06-29
This is an excellent book. It shows you with detailed pictures and explanations exactly how to use electonic components. Very easy to read and understand. I use it as much my son.
Excellent Book, Highly Recommended.......2007-06-17
This is an author who has the beginner in mind. Everything is well explained, no guess work. Pictures and diagrams everywhere. Extremely useful especially if you have low experience in hand work like if you're just out of college. I learns alot about tools, materials, and construction tips that I did not even hear about before. It's not only useful for getting started in robotics but also in electronics. I have an engineering education so I don't know if the author's claim of the information being self contained and no background necessary is right, but in any case, you will get a hell lots of information.
Just what I was looking for!.......2007-06-15
I'm just starting into robotics and have no knowlwdge of electronics. I was thus looking for a beginners book needed to get started. I found that this book "Robot Building for Beginners" to be the best book to start with, even better than the "Robot Building for Dummies". Having got a good start with "Robot Building for Beginners" I am looking forward to reading the next book in the series for more advanced topics.
Fantastic book for jumpstarting a new hobby!.......2007-03-27
My 8-year-old son and I worked our way through this book cover to cover, and found real value on every page. It's intent is clearly on guiding the uninitiated through the design and construction of their very first robot. Along the way, it offers tons of really practical advice on how to pick out the right equipment, where to get the components you'll need, what various components do, and how to work with them. When we were done we had a pretty cool little line following robot, but more importantly we understood how it worked, why it worked, and we had the tools and know-how to take the next step toward designing and building our own creations.
Book Description
Accomplished authors, Preece, Rogers and Sharp, have written a key new textbook on this core subject area. Interaction Design deals with a broad scope of issues, topics and paradigms that has traditionally been the scope of Human-Computer Interaction (HCI) and Interaction Design (ID). The book covers psychological and social aspects of users, interaction styles, user requirements, design approaches, usability and evaluation, traditional and future interface paradigms and the role of theory in informing design. The topics will be grounded in the design process and the aim is to present relevant issues in an integrated and coherent way, rather than assembling a collection of chapters on individual HCI topics.
KEY FEATURES:
* This truly integrated approach to HCI provides students with background information from psychology, sociology, anthropology, information systems and computer science
* Provides principles and skills for designing any technology through the use of many interesting and state of the art examples
* The author supported, highly interactive Web Site provides resources that allow students to collaborate on experiments, participate in design competitions, collaborate on design, find resources and communicate with others
* The accompanying Web Site also features examples, step-by-step exercises and templates for questionnaires
CONTENTS:
Preface
1. What is interaction design?
Interview with Gitta Saloman
2. Understanding and conceptualizing interaction
Interview with Terry Winograd
3. Understanding users
4. Understanding and designing for collaboration and communication
Interview with Abigail Sellen
5. Understanding how interfaces affect users
6. The process of interaction design
Interview with Gillian Crampton Smith
7. Identifying needs and establishing requirements
Interview with Suzanne Robertson
8. Design, prototyping and construction
9. User-centered approaches to interaction design
Interview with Karen Holtzblatt
10. Introducing evaluation
11. A framework for evaluation
12. Observing users
Interview with Sara Bly
13. Asking users and experts
Interview with Jakob Nielsen
14. Testing and modeling users
Interview with Ben Shneiderman
15. Doing design and evaluation in the real world: communicators and advisory systems
Epilogue
Glossary
Customer Reviews:
Desining Interaction Design.......2007-09-23
So, I bought this book "Interaction Design, 2nd Edition" by Sharp, Rogers and Preece, and I thought maybe the world is interested in my two cents. So here it goes ;-)
Despite the fact that the authors use a lot of words in order to explain even the simplest facts, the book is written concisely and presents a clearly understandable train of thought that leads from the very first introductory page to the very last reference page. The authors start out by giving small introductions to every chapter, explaining what is covered on the following pages. A large amount of boxes with extra information and "comments" help to deepen the insight of the covered material. Many pictures, drawings and graphs visualize very abstract sections and a (very) short summary after each chapter is of great value when you would like to freshen up what you have read earlier. The book's layout clearly is geared towards students, as it incorporates many colored boxes and pictures, without, however, being inconsistent. This makes it possible to skim through the book and look up some information without reading through entire sections. Experts in Human-Computer Interaction might wish for a lower amount of examples to bring a point across and would like the authors to have focused on the details a little more. Also, the fact that the authors make a lot of inline citations gives rise to the impression that they only repeat other's ideas. Yet, in fact, the book incorporates a fair amount of the authors' research (as one can easily see in the references), which is presented most modestly.
The book is literally on interaction design, NOT usability testing and NOT on other HCI methodology. If one is interested in that, I would strongly recommend Kuniavsky (2003) and Rubin (1994). If you are interested in interaction design and the principles that lead to a good user experience, this book is for you. Especially novices in ID will appreciate the level of detail, the amount of examples and extra information and the thorough explanations. Experts will find this book helpful and invaluable, yet sometimes a little wordy.
An essential introduction to HCI.......2007-05-16
I think this book is a must-read, essential of the introduction to HCI.
It covers all the important aspects of interaction design with an important and coherent message--user-centeredness for design. It is not a mere collection of the elements that need to be learned in HCI. The authors emphasize the importance of user-centeredness in interaction design, and exhaustively and thoughtfully integrate all the knowledge that is essential to this approach in the field of HCI.
I would strongly recommend this book to any serious instructors for HCI who like to teach the state-of-art directions and approaches in user-centered interaction design in HCI with covering all the important concepts founded in cognitive psychology, software development and design methods, and various user study techniques to be used in interaction design.
I barely spent time on this textbook........2006-12-23
It was somehow helpful to understand on some topics.
However, Interaction Design is kind of subjective topic.
Hence, I think this book is quite good on giving some examples to make a clear point.
Disappointed.......2006-11-11
there were almost 20 pages missing
on both chapters 8 and 9
this is very disappointing...
One of the Groundbreaking Books on HCI..........2006-11-06
If you work in application design or development, software, or web design... READ THIS BOOK! It will take your work to the next level.
Be warned, though... it is terribly boring! You'll think you're in college again. Don't put it down though, it's important knowledge.
Book Description
The fifth edition of this book continues to provide a balanced perspective on the language schools, theories, and applications of artificial intelligence. These diverse branches are unified through detailed discussions of AI's theoretical foundations. The book is broken down into six parts to provide readers complete coverage of AI. It begins by introducing AI concepts, moves into a discussion on the research tools needs for AI problem solving, and then demonstrates representations for AI and knowledge-sensitive problem solving. The second half of the book offers an extensive presentation of issues in machine learning, continues presenting important AI application areas, and presents Lisp and Prolog to the reader. This book is appropriate for programmers both as an introduction to and a reference of the theoretical foundations of artificial intelligence.
Customer Reviews:
Superficial and unclear.......2005-05-27
Trying to gather the greatest audience possible, this book is superficial, completly unclear and boring. Why? Topics are quickly introduced, concepts are rarely analized deeply, it's more discorsive than formal. With so many subjects of AI in the same book not enough space can be given to all of them, so most of the chapters are lists of important algorithms or concepts, barely explained. Do you want to verify it? See the table of contents and the number of pages, and try to see how much space can be given to every point... not enough.
Fantastic Introduction to AI.......2005-01-06
This book really stands out among the AI texts (I've read 4 others). First, the language is clear and simple enough for undergrads to grasp. Second, there are consistent examples that pervade the text to help the reader apply each method to an established problem. Third, the explanations of algorithms/structures are crafted and phrased to TEACH, not merely to summarize a bunch of material for reference purposes. Finally, the programming chapters allow the student to realize the material, and really think about the problems by implementing them and hashing out the details.
I cannot complain about any lack of depth - the length already exceeds 900 pages. To those that desire more, look into academic journals - this is an intro. Moreover, robotics, vision, neural nets, and other topics already have their own "forked" research fields, with textbooks of comparable length focusing on those topics alone!
Enjoy! This text is sure to get you started!
this book not cover much.......2003-07-14
I bought this book for my introduction course in AI. I feel that this book has lack of somethings which are very important, neural networks, and Ai and robotics to name a few. I found that the text is very hard to understand. Again he didn't use enough example to explain some of the topics. I am lost reading this book. The book is not well structured and turned me bored after 30 minutes reading it. The reason are, AI term definations are not included as other book do, few visual diagrams, objective is not well defined. Once again, he didn't include introduction/review of what we acpect to learn of each of every chapters. Reading it is like reading a "white bible". Only plain text and unprofessional layout. This book discorage me reading it. I think i should buy other book that have a wider coverage topics in AI and yet easy to understand, consistent with my AI course syllibus and yet easy for my eyes.
Good For Beginners in AI.......2002-12-05
This is a very good book for anyone wanting to get an insight. Good for the first college course in AI too. It introduces the different areas of AI quite well, and develops logic before doing that. Prolog and LISP are also introduced.
The only reason I wouldn't give this book 5 stars is because
1) The Prolog and LISP features aren't all that great. They could have done better than just explaining what they did.
2) There was very little or almost no depth in the material covered. I wanted to go on reading more about the advanced features, but that never happened. So, I had to go to the library and look for something there.
But a great book for a college course. I wouldn't recommend this for a Grad course in CS...A grad student should be knowing beyond what this book covers.
Don't miss it!.......2002-05-24
This is the best general AI book I've seen this far. It introduces all the popular branches of AI clearly. If you are serious about AI, you should own this book...
Book Description
Text Classification, or the task of automatically assigning semantic categories to natural language text, has become one of the key methods for organizing online information. Since hand-coding classification rules is costly or even impractical, most modern approaches employ machine learning techniques to automatically learn text classifiers from examples. However, none of these conventional approaches combines good prediction performance, theoretical understanding, and efficient training algorithms. Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning. Learning To Classify Text Using Support Vector Machines is designed as a reference for researchers and practitioners, and is suitable as a secondary text for graduate-level students in Computer Science within Machine Learning and Language Technology.
Customer Reviews:
The Gold standard.......2007-05-16
This is a must read for anyone beginning to investigate the analysis of meaning in text using computational methods. I found the initial sections were useful in bringing together my thought on many different aspects of the topic.
Wonderful book on the subject.......2005-09-03
This is a Tesis Work, it contains a review and conmparation of several learners. It focuses mainly on SVM.
Average customer rating:
- A technical approach towards causality
- What is the cause of intolerance?
- Important but difficult
- A Pioneering Book on Causality
- A review of "Causality"
|
Causality: Models, Reasoning And Inference
Judea Pearl
Manufacturer: Cambridge University Press
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Similar Items:
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Learning Bayesian Networks
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Probabilistic Reasoning In Intelligent Systems: Networks of Plausible Inference
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Probability Theory: The Logic of Science
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Algebra of Probable Inference
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Bayesian Networks and Decision Graphs (Information Science and Statistics)
ASIN: 0521773628 |
Book Description
Written by one of the pre-eminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, philosophy, cognitive science, and the health and social sciences. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artifical intelligence, business, epidemiology, social science and economics. Students in these areas will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts that traditional texts have tended to evade or make unduly complicated. This book will be of interest to professionals and students in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.
Customer Reviews:
A technical approach towards causality.......2006-01-23
This is a very interesing book that Judea Pearl worte. The topic is currently of general interest for diverse fields as economics, social sciences and biology, however, this book is not intended for practitoners from these field who face a special problem and search for a possible solution. If you want to buy this book for this reason you will not be able to extract this information for this book. The reason therefor is that important technics like Bayesian Networks or Structural Equations are treated in 3 pages in each case. Judea Pearl assumes that the reader is already familiar with such methods beforehand. (Readers interested in the later subject are strongly refered to Bollen's book "Structural Equations with latent variables".)
Moreover, I do not think that this book presents state of the art information about our current knowledge of this subject. For example, the important problem to extract a network structure (structure learning) from data rather than estimating the parameters of a given networks structure is completely missing.
Nevertheless, this is a good book, because it might give you in the long run (you can not read it in one piece) insights you did not have before. Of course not to all topics causality is involved (see, e.g., above) but the given topics are thorough explained albeit on an advanced level.
Update: I add one star (total three) to my evaluation, because in the meanwhile I appreciate the historical development described in the book including references to the literature.
What is the cause of intolerance?.......2004-12-10
Pearl included an Epilogue containing a lecture he gave in 1996 entitled, "The Art and Science of Cause and Effect."
Pearl concludes the lecture by comparing his theory of causality to the first mathematical tool,the abacus: "But the really challenging problems are still ahead: We still do not have a causal understanding of poverty and cancer and intolerance, and only the accumulation of data and the insight of great minds will eventually lead to such understanding. "The data is all over the place, the insight is yours, and now an abacus is at your disposal, too. I hope the combination amplifies each of these components."
Unfortunately, virtually no advances have been made in learning the causation of intolerance nor how to rid us of it. And Judea Pearl suffered immensely because of that . . . Daniel Pearl, his son, was killed in Iraq due to intolerance. :-(
BTW, the book is great.
Important but difficult.......2004-09-16
The scientific research community has adopted rigorous methods to eliminate the need for subjective judgments about many things, but when it comes to testing whether X causes Y, they revert to intuition and hand-waving. This book makes a strong argument that we shouldn't accept that. It demonstrates that it is possible to turn intuitions about causation into hypotheses that are unambiguous and testable.
But the style is sufficiently dense and dry we will need some additional books with more practical styles before these ideas become widely understood. The style is fairly good by the standards of books whose main goal is rigorous proof, but it's still hard work to learn a large number of new concepts that are mostly referred to by terse symbols whose meaning can't be found via a glossary or index. Pearl occasionally introduces a memorable word, such as do(x), the way a software engineer who wants readable code would, but mostly sticks to single-character symbols that seem unreasonably hard (at least for us programmers who are used to descriptive names) to remember.
If you're uncertain whether reading this book is worth the effort, I strongly recommend reading the afterword first. It ought to have been used as the introduction, and without it many readers will be left wondering why they should believe they will be rewarded for slogging through so much dry material.
A Pioneering Book on Causality.......2003-04-08
This is a pioneering book dealing exhaustively with the subject of causation. Its contribution to the field of "Uncertainty in AI" is unmeasureable. It dealt with graphical models for reasoning in depth. For computer scientists looking for an encyclopedia of algorithms and applications on causation, there can not be a better book. I highly recommend this book for researchers in UAI. A word of caution: This is not a book for starters and those who do not have a well developed concept of uncertainty.
A review of "Causality".......2002-05-26
First off, the rating of three stars is relative to my expectations that this book would provide me with some insights in how to use graphical models for purposes of making inferences from statistical data and, in general, to facilitate the process of (machine) learning from data. And although Pearl and his colleagues have made great progress in this area, this book seems more targeted for researchers in areas outside of AI, such as economics, statistics, and medical research. Although the author gives a number of rigorous definitions to help support his notions of causality, the book is written in a somewhat abstract manner with few if any nontrivial examples (although enough trivial ones to satisfy a more general audience) to support the definitions and concepts. References to the literature are favored over mathematical proofs. Thus, aside from the references, I found this book of little use, but on the other hand, I do recommend it for its intended audience, for I do believe that graphical models can be of great use in these other areas.
Finally given the controversy and general misunderstanding about "causality", I wonder why Pearl would even use definitions like "causal model" and "...variable X is a causal influence of variable Y". His justification seems that researchers still think in terms of cause and effect, and thus it would serve them well if they had a mathematical foundation to fall back on.
Even if I did not have issue with some of the techniques and algorithms endorsed in this book, it would still seem much more appropriate to supply fresh, distinguished definitions (devoid of the "cause" word and its synonyms) and thus when future researchers use and make reference to Pearl's structural methods, they will call them as such and hopefully avoid confusion and controversy.
Book Description
Knowledge representation is at the very core of a radical idea for understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top down, putting the focus on what an agent needs to know in order to behave intelligently, how this knowledge can be represented symbolically, and how automated reasoning procedures can make this knowledge available as needed.
This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a lucid and compelling way. Each of the various styles of representation is presented in a simple and intuitive form, and the basics of reasoning with that representation are explained in detail. This approach gives readers a solid foundation for understanding the more advanced work found in the research literature. The presentation is clear enough to be accessible to a broad audience, including researchers and practitioners in database management, information retrieval, and object-oriented systems as well as artificial intelligence. This book provides the foundation in knowledge representation and reasoning that every AI practitioner needs.
*Authors are well-recognized experts in the field who have applied the techniques to real-world problems
* Presents the core ideas of KR&R in a simple straight forward approach, independent of the quirks of research systems
*Offers the first true synthesis of the field in over a decade
Customer Reviews:
how to reason about knowledge.......2006-03-13
Brachman and Levesque describe what may be considered the foundations of artificial intelligence. They look at how to represent knowledge. As far more than just a passive collection of facts. The "Reasoning" in the book's title is the important aspect of that representation. You can understand how to apply reasoning in a logical and systematic manner.
Those of you from a computing background may also find similarities between the discussion of object oriented representations and various OO programming languages. You might actually key off these similarities by using the text to guide your KR coding.
Don't have to be a math buff to understand.......2005-06-24
I came across this book looking for a text that would explain the context of First Order Logic, why it is used for so many knowledge representation problems, how it is used to solve them, and its limitations. I must say that this is far and away the best book I've found to answer these questions. If you search around a little at the competition, you will find much of the text quickly turning to mathematical proofs and deductions in their explanations. While this is of course necessary and helpful, it doesn't (for me) really give an idea of how and why these methods are used practically. You can tell that these authors spent some time on ensuring consistency and fluency of the writing, which I find so very helpful.
I'm trying to think of something bad to say about it: I wish it were longer! If you read the preface you will see the authors call it an introduction, which is definitely true. Maybe they will team up again for a more in-depth text on some aspect of this subject.
This book is an Eye-Opener!.......2004-11-25
I love this book- It is a comprehensive introduction into knowledge representation, with enough detail to create your own knowledge representation programs.
Are you a programmer who wonders what it really means when an object *IS* another object, in the form of inheritance found in object-oriented systems? Ever confused by the nuances of multiple inheritance? Ever wonder what XML or OOP or Relational Databases have to do with each other? Ever wonder if all those A.I. programmers in the 70s actually created anything useful? Ever wonder how type systems work? Ever wonder how to store complicated and vague data into a database?
This book doesn't really have answers to these questions (nobody really does, in my opinion) but learning the information in this book is the first step you'll want to take to get closer to some answers...
It basically covers 3 main topics: FOL (traditional logic like you probably learned in college) Frames (sort of the grandaddy of OOP) and Description Logics (a really powerful synthesis of object-thinking with strict logical fundamentals)
This book has a bit of hairy mathematical notation in it, so if your not comfortable talking about things like "an object x that is an element in the domain" some of the chapters will require a bit of effort on your part. The authors are careful, however, to follow every difficult mathematical analysis with some concrete examples that ease the learning process- I often wish examples were more frequent in other theoretical tombs like this. Any computer programmer can process this text with a bit of moderate effort.
I couldn't imagine being a professional programmer and not knowing the information in this book now that I have read it. Although the topics in this book are somewhat obscure today, I think they will receive far greater appreciation in the future- especially among medical software developers. Here's your chance to be ahead of the curve in the field of knowledge representation!
Book Description
This exciting addition to the McGraw-Hill Series in Computer Science focuses on the concepts and techniques that contribute to the rapidly changing field of machine learning--including probability and statistics, artificial intelligence, and neural networks--unifying them all in a logical and coherent manner. Machine Learning serves as a useful reference tool for software developers and researchers, as well as an outstanding text for college students.
Customer Reviews:
Outstanding.......2007-09-12
I read this book about 7 years ago while in the PhD program at Stanford University. I consider this book not only the best Machine Learning book, but one of the best books in all of Computer Science. It covers every branch of ML I know of and it covers it really well. I found Mitchell's chapter on Neural Networks more insightful than an entire book on NN's that I read. I also found his chapter on Reinforcement Learning more useful and better explained than an entire book on Reinforcement Learning that I also read. The other chapters cover other ML topics at the same level of quality and rigor.
The author did an amazing job in covering the breadth and depth of ML in less than 500 pages. I hope he will write a new edition to cover the advances that happened in the last decade.
Great Start to Machine Learning.......2007-08-27
I have used this book during my masters and found it to be an extremely helpful and a gentle introduction to the thick and things of machine learning applications. The various chapters are nicely paced with helpful problems at the end. Another great thing about the book is treatment of detailed examples with each concept and that the author carefully ties various concepts as they arise, with not just new, but also examples from previous chapters, which helps the user to understand different concepts applied to same problems thereby making clear difference between different methods. Also the author has a dedicated website with updated errata and notes, which is also very helpful! Having said that, I think the book is an introduction to various machine learning methods and one can easily follow on the references listed for detailed treatment of relevant topics.
Best book I've seen on topic.......2007-01-31
I have this book listed as one of the best and most interesting I've ever read. I loved the book just as much as I loved the course we used it in.
I have a genuine interest in AI and especially Machine Learning and this book both inspired me as well as clared some things up for me. Like how the spectrum of different known methods differ in their appoach of different problems.
This book is also very concise and well written, without being too mathematical. Making it very easy to read and understand.
Ever since I took that course I keep returning to this book as a reference when I have a related problem to solve, or just bothering my mind.
Highly recommended!
too expensive I would say.......2006-10-13
great book if you wanna start sth anywhere in machine learning, but it is toooooo expensive.
Excellent book, concise and readable.......2006-06-22
This is a great book if you're starting out with machine learning. It's rare to come across a book like this that is very well written and has technical depth. The writing is to the point, maybe even a bit terse, but all that you need to know is in there. It's a bit old so doesn't cover kernel methods or SVM's but is still a great first machine learning book.
Book Description
This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.
Customer Reviews:
An extremely good book.......2006-11-16
This is a very good book. Another reviewer has commented on Vojislav Kecman being an excellent teacher. I whole-heartedly second that opinion. Often times, while reading this book, you will pause with a doubt or question. What you will find surprising is that almost certainly the author has answered that question in the next paragraph. Many times, the author's answers will tally your own answers.
The first chapter of the book (entitled: Learning and Soft Computing: Rationale, Motivations, Needs, Basics) is 119 pages long. It is an essential reading. By the time you finish reading this chapter the things will start falling into place and you will be more motivated and ready to read the remaining chapters. Until you are highly aware of this topic, do not skip this chapter.
A book is made up of a lot of things other than the text that it covers. Does it contain many/any stupid jokes? Is it printed on the highest quality paper? Is the font size good? Is it printed too dense? Is the cover page inviting enough? Are the dimensions/weight of the book correct? On all these counts the book scores high.
Consistent with the subject matter that it covers, this is not an easy book. You will perhaps like to read it with paper and pencil. But if you are willing to spend time with this book, this book will do a lot of good to you. This is a very good book.
An excellent book on Machine Learning.......2003-02-27
What strikes me each time I open this book is Mr Kecman's sense of pedagogy: it is a lesson in the matter. Not only his book delivers the - sometimes complex - techniques in a highly readable manner, but the concepts behind each of the main tools (SVM, NN & FL) he chose to highlight are always brilliantly put in context. One comes out of the reading with more than a set of equations but rather with a clearer picture of the field.
Mr Kecman is - without a doubt - a great teacher.
This effort to deliver a clear message is furthermore underlined through the numerous original figures: if you are like me and feel that a (good) picture speaks more than a thousand words, you will sure appreciate the way the illustrations complement the text and truly help the understanding.
I have read several other books on the subject but if I had to chose one for teaching purposes, this would be the one. I you want to build a better understanding of the field, get this book: it will pay on the long term.
Excellent, useful book!.......2001-07-23
This book is a nice and, I would say, a successful attempt to provide a unified survey of important theoretical and practical machine learning tools: neural networks (NN), support vector machines (SVM) and fuzzy systems (FS).
Book consists of nine chapters, covering SVMs, one- and multi-layer perceptrons and radial-basis function networks, as variants of neural networks, and basics of fuzzy theory. This is followed by interesting case-studies (in financial, control and computer graphic applications) and concluded by basics of optimization theory and an overview of necessary mathematical tools. All the MATLAB programs needed for the simulated experiments are available on the book web site.
Authored by Vojislav Kecman, a prominent researcher in the field of soft computing and previous MIT visiting professor, this book is an excellent material for advanced undergraduate and introductory graduate courses in machine learning applications and soft computing....
Amazon.com
David Goldberg's Genetic Algorithms in Search, Optimization and Machine Learning is by far the bestselling introduction to genetic algorithms. Goldberg is one of the preeminent researchers in the field--he has published over 100 research articles on genetic algorithms and is a student of John Holland, the father of genetic algorithms--and his deep understanding of the material shines through. The book contains a complete listing of a simple genetic algorithm in Pascal, which C programmers can easily understand. The book covers all of the important topics in the field, including crossover, mutation, classifier systems, and fitness scaling, giving a novice with a computer science background enough information to implement a genetic algorithm and describe genetic algorithms to a friend.
Customer Reviews:
Great start to your journey in Genetic Algorithms........2007-03-09
This is a great book to begin your journey on Genetic Algorithms (GA). The author is a pioneering authority on the subject and has explained the basics of a GA in a very gentle and easy to understand manner. The book has a great variety of specific but diverse examples, which may not be useful at first glance, but gives an insight to where all the technique has been applied!
However, some aspects of the book perhaps need an edition, like the more recent advances in GA operators, specifics of chromosomal representation schemes, non-linear optimization functions, etc. I have read several, well written books on the subject, but this one has a very distinct and sometimes interesting style of writing! The best would be to quickly read this one to get a fairly good understanding of the basics and then take up a recent book that addresses other aspects like Mitchell's book, for example.
Having said that, I think the book is a great and inspiring start to using genetic algorithms.
Genetic Algorithms in Search, Optimization, and Machine Learning by David E. Goldberg.......2006-07-06
Excellent book for Graduate students and instructors. Highly recommend!
Not the only paradigm for evolutionary computation.......2005-07-19
This book gives a good introduction to genetic algorithms for a general undergraduate audience. However, it is important to note that it does not cover Evolutionary Strategies, an approach to evolutionary computing that I have found quite useful since it is specifically designed for Euclidean space optimization problems where many if not most interesting optimization problems are formulated in (take for example the problem of determining the weights of a neural network that minimizes the network's overall classification error). Nor does it cover evolutionary programming (not to be confused with genetic programming). So after reading this book, I recommend (for the mathematically adventurous) Thomas Back's "Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms"
ISBN: 0195099710
Happy reading and enjoy the fascinating world of evolutionary computation!
Read a review article instead!.......2004-11-06
I agree with another reviewer who said the book was unnecessarily long. Genetic Algorithms are a great programming tool, and there are some tips and tricks that can help your programs converge faster and more accurately, but this book had a lot of redundant information.
If you are interested in using GA for solution-finding, I doubt you'll find much useful in this book beyond the first chapter or so. Many of the examples later in the book were so specific that I couldn't see how they could be usefully generalized. Really optimizing a GA approach for a specific problem domain takes a fair amount of tuning, and this book won't help much with that.
I think time spent surfing siteseer or other publication sites would be better spent than reading this book.
Needs updating.......2004-09-03
OK, I agree with the previous reviewers: it's the classical textbook for GAs. But it definitely needs updating, as it's a 15-year old book and much has been done in the area. Niching methods, for example, are just outlined. I'd recommend Melanie Mitchell's book instead of this one.
Book Description
Sowa integrates logic, philosophy, linguistics, and computer science into this study of knowledge and its various models and implementations. His definitive new book shows how techniques of artificial intelligence, database design, and object-oriented programming help make knowledge explicit in a form that computer systems can use.
Customer Reviews:
A Unique Contribution.......2005-08-15
In this book, John Sowa conveys diverse and effective insights within the field of knowledge representation (KR). The frameworks he employs are deeply grounded philosphically. (Sowa's previous work on conceptual structures reactivated and extended the innovative work of Charles Sanders Peirce integrating logic and graph theory.) The work reviewed here surveys a wide range of KR issues from basic ontology to agency and processes. Chapter 6, Knowledge Soup, is widely recognized for framing and addressing some of the more demanding, and largely unresolved, challenges in the field.
Throughout the book, issues are explored in a coherent, readable way. Of course, KR implies the use of relevant formalisms, and readers with some background in some AI research tradition will be better prepared to absorb the book's insights. However, for students and scholars looking for an integrated overview, Sowa makes a unique contribution.
Good, but suffers from unnecessary complexity........2000-09-07
I thought the first three chapters did an excellent job of covering advances in knowledge representation. However chapter four is marred by an attempt to present what appears to be virtually every syntax used relating to processes. Somewhere in this gulf of complexity I think he has some basic concepts, but they are hard to reach. It's equivalent to reading a book on algorithms in which the author presents the algorithms in C, Cobol, Fortran, Basic, SAS, etc. Why not just present the concepts within the context of a MINIMUM of syntax? Still the book is worth reading and has good appendixes.
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