Reviews (1)

New Text on Pattern Recognition/Machine Learning
I have been working in the field of signal processing and speech for more
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Subjects:  1. Artificial Intelligence - General    2. Computer Books: General    3. Computer Vision    4. Computers    5. Computers - General Information    6. Artificial intelligence    7. Computers / Computer Vision    8. Computing and Information Technology   

Reviews (14)

Words words and more words but little substance
I bought this book based on all the reviews posted on Amazon.com. I must say I was extremely disappointed. The book does not describe even a single machine learning algorithm adequately - no pseudocode, no mathematical description, and at the end of the day, no real understanding. If you are an analytically challenged manager who wants to pick up a bunch of buzzwords to throw around to impress your equally analytically challeged colleagues or customers who don't have a clue, this might be the book for you - a n overpriced, highly overrated "data mining for dummies". If you want to learn something about machine learning, well.. stick to books like Machine Learning by Tom Mitchell or Elements of Statistical Learning by Hastie, Tibshirani, and Friedman.

Very helpful
The major virtue of this book is the emphasis on practical applications and bread-and-butter techniques for accomplishing tasks that one could expect in a business environment. That is not to say that these techniques could not be used in a scientific research environment. They indeed could be, and in fact may be even easier to implement due to the long time scales that are available in research environments for processing information. In the business world however data mining has proven to be an activity that gives a substantial competitive edge, and so many businesses are seeking even more sophisticated methods of data mining and Web mining. Data mining could easily be considered to a branch of artificial intelligence (AI), due to its emphasis on learning patterns and performing classification, and the learning and classification tools it uses were discovered by individuals who would describe themselves as being researchers in artificial intelligence. But many, and it is fair to include the authors of this book, do not want to view data mining as part of artificial intelligence, since the latter stirs up discussions on the origin of intelligence, autonomous robots, and conscious machines, to paraphrase a line from chapter 8 of this book. The authors make it a point to emphasize that data mining, or "machine learning" is concerned with the algorithms for the inference of structure from data and the validation of that structure.
Lucid
I'm surprisingly please with this book. I've been reading up on the topic and associated algorithms in other books for some time; I'm a software developer but don't have a statistics background, and so felt a lot of the texts were too focused on the math and the theory while being thin on content when it came to "rubber hitting the road", or even using clear, simple examples and straight-forward notation.
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Subjects:  1. Artificial Intelligence - General    2. Computer Books: Database    3. Computers    4. Computers - General Information    5. Data mining    6. Database Management - Database Mining    7. Information Storage & Retrieval    8. Computers / Database Management / Data Mining    9. Databases & data structures   

Reviews (18)

Too wordy
Having to go thru some of the data mining such as CART, I find the book rather wordy.Sometimes, it takes a couple of readings to understand, too many termiologies.I think a lot of stuff might be better illustrated with mathematical formulae rather than words or both.For e.g., I was trying to understand what is surrogate variable, getting a big picture from the English is okay, but to actually compute it, I find information not as clear.This book is written in a way ... seem to be theoretical, and yet not rigorous enough.
Very complete
This book is a very interesting book to learn the main statistical approach of data mining. It's clear and full of examples. If you go a Stanford data mining website you will find all the courses and exercises linked to the book.
El libro de la mineria de datos
Desde el punto de vista tecnico este libro es sumamente completo ya resume muy bien muchos de los metodos estadistico que se aplican en la mineria de datos. Pero a mi parecer este libro es debil en cuanto a la descripcion de ciertos algortimos que describe, y en algunos casos solo se menciona el procedimiento como una formula (la matriz S de los trazadores cubicos) ya que presupone que el lector ya tiene un buen conocimiento sobre trazadores cubicos.
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Subjects:  1. Artificial Intelligence - General    2. Computers - General Information    3. Database Management - Database Mining    4. Mathematics    5. Neural Computing    6. Probability & Statistics - General    7. Regression Analysis    8. Science/Mathematics    9. Supervised learning (Machine l    10. Supervised learning (Machine learning)    11. Data Mining    12. Inference    13. Mathematics / Statistics    14. Neural Nets    15. Prediction    16. Probability & statistics    17. Statistical Learning   

Reviews (16)

Good Theoretical GA Textbook
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.
Not for beginners
I have an engineering degree, and I found this to be a little tough to follow for two reasons: An introduction and much more
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.Read more

Subjects:  1. Artificial Intelligence - General    2. Computer Science    3. Computers    4. Computers - General Information    5. Programming - General    6. Science/Mathematics    7. Applied ecology    8. Biology, Life Sciences    9. Computer modelling & simulation    10. Computers / Computer Science    11. Machine learning   

Editorial Review

"Intended as an upper-level undergraduate or introductory graduate text in computer science theory," this book lucidly covers the key concepts and theorems of the theory of computation. The presentation is remarkably clear; for example, the "proof idea," which offers the reader an intuitive feel for how the proof was constructed, accompanies many of the theorems and a proof. Read more

Reviews (45)

Terrible
I do not understand why this book is rated so highly.
Most appropriate for CS students
As a teacher of the subject, I have had the chance to evaluate numerous books on the theory of computation.Of all the available texts, I think this one is the most appropriate for CS students.In the past I taught out of Dexter Kozen's book, which is incredibly elegant, but had some resistance from the students.Thinking it over I decided that Kozen's text, although beautiful, may be better suited to students pursuing a degree in pure math.Sipser's book, on the other hand, is more gentle.I find that Sipser demands far less mathematical maturity from his readers, and thus allows the difficulty to be shifted from excessive formalism to the inherent challenges present in the material.In addition, following Sipser's treatment, I was able to cover finite state machines and pushdown automata in far less time, thus allowing me to concentrate on computability and beyond.The book really shines in its treatment of computability theory, eloquently directing attention to some of the most beautiful aspects.
Excellent accessible textbook on the theory of computation
The theory of computation is the branch of computer science that deals with whether and how efficiently problems can be solved on a computer. The field is divided into two major branches: computability theory and complexity theory, but both branches deal with formal models of computation, and both of these subjects are dealt with in this book. This is an important subject because no matter what leaps forward computers make, something that is proved undecidable and not computable will always be so, thus the theory behind this subject is very important.
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Subjects:  1. Advanced    2. Computational complexity    3. Computers - General Information    4. Discrete Mathematics    5. General    6. Logic    7. Machine Learning    8. Machine theory    9. Mathematics    10. Science/Mathematics    11. Systems Analysis    12. Computers / Information Theory    13. Mathematical theory of computation   

Reviews (4)

In depth review of kernel methods in machine learning
Great book, but a word of caution, it is not for the novice.
best book of kernel methods
It is the best book on kernel methods. It covers a wide range of subjects. interesting introduction to support vector machine learning
The authors are young researchers who did their Ph.D. research in this rapidly developing branch of pattern recognition. Because they are young and are at the state of the art in the filed the book has sevral advantages and disadvantages and what I see as a disadvantage someone else might view as an advantage. Anyway here is my view.Read more

Subjects:  1. Algorithms    2. Algorithms (Computer Programming)    3. Artificial Intelligence - General    4. Computer Books: General    5. Computer Science    6. Computers    7. Computers - Other Applications    8. General    9. Kernel functions    10. Machine Learning    11. Programming - General    12. Computers / Computer Science    13. Databases & data structures    14. Mathematical theory of computation   

Reviews (7)

More for mathematicians than computer scientist
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).
A little dry.
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...
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..
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Subjects:  1. Algorithms    2. Algorithms (Computer Programming)    3. Artificial Intelligence - General    4. Computer Books: General    5. Computers    6. Computers - General Information    7. General    8. Kernel functions    9. Machine Learning    10. Programming - General    11. Computers / Bioinformatics    12. Data capture & analysis    13. Pattern recognition   

Reviews (27)

too expensive I would say
great book if you wanna start sth anywhere in machine learning, but it is toooooo expensive.

Excellent book, concise and readable
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.

great book
This is a great book because it focuses on machine learning techniques. It has been used as textbook in my class. ... Read more

Subjects:  1. Algorithms (Computer Programming)    2. Artificial Intelligence - General    3. Computer Bks - General Information    4. Computer Books: General    5. Computer Science    6. Computer algorithms    7. Computers    8. Computers - General Information    9. Machine Learning    10. Non-Classifiable   

Reviews (5)

Nice introduction, but no more
Well, at first I was petrified to find a book that sounded like it deeply explores the subject of kernel methods. But all in all, it did not quite achieve what I hoped for. As a practical approach, when it comes to implementation, it serves nicely as a reference. The deeper mathematical roots of kernels (especially when it comes to measure theory and functional analysis) are not dealt with at all or just scratched at the very surface. The notation is sometimes awkward, mentioning for example the representation of an object in a given vector space with respect to the basis. And: Too much copied and pasted from the former book about SVMs. Basically, reading papers of Carmeli, Aronszajn and others will give you a much deeper insight into the subject.

coherent and accessible reference, ready-to-use algorithms
This work presents a coherent overview of an important field in machine learning. The unifying framework of kernel methods has proven state of the art results and the community has been waiting for a book like this to make both theory and practice of kernel methods accesssible for readers of all different backgrounds (researchers, students, practioners from both academia and industry, ...).
A Useful Reference on Kernel Methods
The book is divided into 3 parts. The theory is all in part I,
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Subjects:  1. Algorithms    2. Computer Books: Languages    3. Computers    4. Data processing    5. General    6. Kernel functions    7. Machine learning    8. Mathematics    9. Pattern perception    10. Programming - Systems Analysis & Design    11. Computer Software Packages    12. Computers / Bioinformatics    13. Pattern recognition   

Reviews (4)

New to Field of Learning Theory
I am relatively new to statistical learning theory, though with a solid background in supporting theories and a Master's in Engineering. I found the text readable. I appreciate the historical perspective and the development of concepts by the author. I was generally able to grasp Vapnick's theories and explanations, though often after rereading passages many times.
worth reading
A good, albeit highly idiosyncratic, guide to Statistical Learning. The highly personal account of the theory is both the strong point and the drawback of the treatise. On one side, Vapnick never loses sight of the big picture, and gives illuminating insights and formulations of the "basic problems" (as he calls them), that are not found in any other book. The lack of proofs and the slightly erratic organization of the topic make for a brisk, enjoyable reading. On the minus side, the choice of the topics is very biased. In this respect, the book is a self-congratulatory tribute by the author to himself: it appears that the foundations of statistical learning were single-handedly laid by him and his collaborators. This is not really the case. Consistency of the Empircal Risk Measure is rather trivial from the viewpoint of a personal trained in asymptotic statistics, and interval estimators for finite data sets are the subject of much advanced statistical literature. Finally, SVMs and neural nets are just a part of the story, and probably not the most interesting. A very nice book to get ideas on support vector machines
This is a very readable book by an authority on this subject. The bookstarts with the statistical learning theory, pioneered by the author andco-worker's work, and gradually leads to the path of discovery of supportvector machines. An excellent and distinctive property of support vectormachines is that they are robust to small data perturbation and have goodgeneralization ability with function complexity being controlled by VCdimension. The treatment of nonlinear kernel classification and regressionis given for the first time in the first edition.The 2nd edition includessignificant updates including a separate chapter on support vectorregression as well as a section on logistic regression using the supportvector approach. Most computations involved in this book can be implementedusing a quadratic programming package. The connections of support vectormachines to traditional statisticalmodeling such as kernel density andregression and model selection are also discussed. Thus, this book will bean excellent starting point for learning support vector machines. ... Read more

Subjects:  1. Computational learning theory    2. Computer Science    3. Computers    4. Cybernetics    5. Information Theory    6. Mathematics    7. Probability & Statistics - General    8. Reasoning    9. Science/Mathematics    10. Machine learning    11. Mathematics / Statistics    12. Mathematics for scientists & engineers    13. Probability & statistics    14. Statistical Learning    15. Statistical Theory   

Reviews (4)

Great Machine Learning Overview Book
I have a little knowledge about some areas of Machine Learning; I have found this book to be a very useful reference for the areas that I am not familiar with.
Great Introduction
I was very happy with this book.The author used good judgement when deciding the level of detail to delve into for each concept.I was not brand new to machine learning but I still got alot out of the book.

Good one to start
I would like to congratulate the author on writing this book, which is crisp and covers whole range of topics. What I liked the most is a systematic disucssion on a wide variety of areas in machine learning with a certain degree of details.
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Subjects:  1. Computer Books: General    2. Computer Science    3. Computers    4. Computers - General Information    5. Machine Theory    6. Computers / Machine Theory    7. Machine learning   

Reviews (18)

Needs a second volume which explains the first
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
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
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. Read more

Subjects:  1. Artificial Intelligence - General    2. Automatic speech recognition    3. Computational Linguistics    4. Computer Books: General    5. Computer Vision    6. Language Arts & Disciplines    7. Language Arts / Linguistics / Literacy    8. Linguistics    9. Audio processing: speech recognition & synthesis    10. Computers / Computer Vision    11. Machine learning    12. Natural language & machine translation   

Editorial Review

David Goldberg's Read more

Reviews (18)

Genetic Algorithms in Search, Optimization, and Machine Learning by David E. Goldberg
Excellent book for Graduate students and instructors. Highly recommend!

Not the only paradigm for evolutionary computation
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 usefulsince 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"
Read a review article instead!
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.
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Subjects:  1. Artificial Intelligence - General    2. Combinatorics    3. Computer Bks - General Information    4. Computer Books: Operating Systems    5. Computers - General Information    6. Discrete Mathematics    7. Genetic algorithms    8. Machine learning    9. Optimization (Mathematical Theory)    10. Computers / Programming / Algorithms   

Subjects:  1. Computer Books: General    2. Computers    3. Computers - General Information    4. Machine Theory    5. Supervised learning (Machine learning)    6. Computers / Machine Theory    7. Machine learning   

Reviews (8)

READ IT BEFORE REVIEWING PLEASE
my five stars are just to counteract the single star from the idiotic reviewer who gave the book one star w/o even bothering to read it. i haven't read this volume yet either, but i just ordered it and it's on its way; i'm certain i won't be disapointed. i'm a programmer and an artist and i use GP effectively to evolve forms both sonic and visual. just because you know your field(s) doesn't mean you can't benefit from a knowledge of evolutionary algorithms, quite the contrary. we have koza and friends to thank for a lot of inspirational work. i for one enjoy the interaction that i have with my algorithms, and since i'm the fitness function, forms crop up which never would have if either i or the machine were working alone. apologies for being guilty of the same crime as said reviewer but i feel in this case it's justified.

And the future is...
Genetic programming is like a new Big Bang in computer universe. Why Should You Buy This Book???
Why this book, when there are several shorter books on GP, and its principle author, John Koza, has written two other, more general and equally voluminous books on GP? This book addresses how to evolve program architecture, that's why! Living organisms didn't grow arms and eyes through simple mutation. It required more subtle genetic operators. Traditional genetic operators (as used in genetic algorithms) may be sufficient for evolving solutions to optimization problems where the structure if not the specifics of each solution is pretty much the same. But to effectively evolve program structures, you need architecture altering genetic operators. This book provides motivations in computer science, foundations in biology, and explanations in English. ... Read more

Subjects:  1. Artificial Intelligence - General    2. Computer Books: General    3. Computers    4. Computers - Languages / Programming    5. Genetic Algorithms    6. Genetic programming (Computer    7. Genetic programming (Computer science)    8. Life Sciences - Genetics & Genomics    9. Circuits & components    10. Computers / Artificial Intelligence    11. Machine learning   

Subjects:  1. Artificial Intelligence - General    2. Computer Books: General    3. Computers    4. Computers - General Information    5. General    6. AI logics    7. Artificial intelligence    8. Computers / Artificial Intelligence    9. algorithmic learning    10. computational intelligence    11. evolutionary computing    12. intelligent agents    13. intelligent information processing    14. knowledge processing    15. machine learning    16. natural language processing    17. neural computing   

Reviews (16)

make sure you are right audience
It's not that this is a bad book, but you have to make sure you are right audience.The book offers very high-level overviews on various techniques of data mining, but it is almost impossible to learn how to really implement them.Since there are no exercises after each chapter you probably already know who the target audience of the book are.

It shows me many examples
Even if it is bad as all the gentlemen said, I think at least it gives me many examples which are not mentioned in other books before.

Very, Very, Very Bad Book !
I am a professional in the field of data mining (over 10 years experience). I am always taking classes and reading on the subject. This book was a required text in a grad class I was taking in the evenings. I was excited because I had heard so many good things about it.
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Subjects:  1. Algorithms (Computer Programming)    2. Artificial Intelligence - General    3. Computer Books: General    4. Computers    5. Computers - Data Base Management    6. Data mining    7. Database Engineering    8. Database Management - Database Mining    9. Database Management - General    10. Programming - General    11. Algorithms & procedures    12. Computers / Artificial Intelligence    13. Databases & data structures    14. Mathematical theory of computation   

Subjects:  1. Artificial Intelligence    2. Artificial Intelligence - General    3. Computer Books: General    4. Computer Programming    5. Computers    6. General    7. Genetic programming (Computer    8. Genetic programming (Computer science)    9. Programming - General    10. Science    11. Computers / Artificial Intelligence    12. Machine learning   

Subjects:  1. Artificial Intelligence - General    2. Automation    3. Computer Vision    4. Control systems    5. Engineering - Mechanical    6. General    7. Medical / Nursing    8. Robot vision    9. Robotics    10. Robots    11. Science/Mathematics    12. Technology    13. Computers / Computer Graphics / General    14. Image processing: graphics (static images)    15. algorithmic learning    16. autonomous agents    17. camera-equiped robots    18. cognitive systems    19. intelligent agents    20. machine learning    21. mobile robots    22. object recognition    23. robot navigation    24. robust control   

Reviews (3)

Very good introduction in causal Modeling
The book by Kor