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Machine Learning Review

Average Reviews:

(More customer reviews)I agree with some of the previous reviews which criticize the book for its lack of depth, but I believe this to be an asset rather than a liability given its target audience (seniors and beginning grad. students). The average college senior typically knows very little about subjects like neural networks, genetic algorithms, or Baysian networks, and this book goes a long way in demystifying these subjects in a very clear, concise, and understandable way. Moreover, the first-year grad. student who is interested in possibly doing research in this field needs more of an overview than to dive deeply into
one of the many branches which themselves have had entire books written about them. This is one of the few if only books where one will find diverse areas of learning (e.g. analytical, reinforcment, Bayesian, neural-network, genetic-algorithmic) all within the same cover.
But more than just an encyclopedic introduction, the author makes a number of connections between the different paradigms. For example, he explains that associated with each paradigm is the notion of an inductive-learning bias, i.e. the underlying assumptions that lend validity to a given learning approach. These end-of-chapter discussions on bias seem very interesting and unique to this book.
Finally, I used this book for part of the reading material for an intro. AI class, and received much positive feedback from the students, although some did find the presentation a bit too abstract for their undergraduate tastes

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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.

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Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems) Review

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(More customer reviews)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.
This book is so well-written that it communicates the concepts clearly, lucidly and in an organized fashion. The section that introduces Bayesian probability was drop-dead simple to follow. Quite frankly, having read a few other treatments on it, I can now say that everything else I read before this was overly complicated. Brevity is the soul of wit, no?
To the reviewer who criticized the authors use of words to describe equations: This is what the authors intended to do. Would you fault them for writing in English if you wanted Greek? Not everyone who can benefit from applied data mining has the requisite background to understand the nitty gritty mathematics, nor should they have to, if they just want to understand the behavior and practical applications of the technology.

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Advanced Computer Architecture: Parallelism, Scalability, Programmability Review

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(More customer reviews)This is an excellent book especially for students who want to do Masters in Computer Science & Engineering. Concept of Parallel processing, multistage Unix Kernel etc. are excellent. More detailed discussion on SuperComputer Architecture with diagrams are required.

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This book deals with advanced computer architecture and parallel programming techniques. The material is suitable for use as a textbook in a one-semester graduate or senior course, offered by Computer Science, Computer Engineering, Electrical Engineering, or Industrial Engineering programs.

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Digital Processing of Speech Signals Review

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(More customer reviews)Good introduction to various speech processing techniques. Attempts were made to cover very broad areas such as speech synthesis, coding, and recognition; which obviously cannot be done in a single volume. The serious readers looking to specialize in any of the mentioned areas must find other sources for more thorough info. Also note that many materials in the book are already outdated due to rapid technological advances.

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The material in this book is intended as a one-semester course in speech processing.The purpose of this text is to show how digital signal processing techniques can be applied to problems related to speech communication. The book gives an extensive description of the physical basis for speech coding including fourier analysis, digital representation and digital and time domain models of the wave form. It goes on to discuss homomorphic speech processing, linear predictive coding and digital processing for machine communication by voice.

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Fundamentals of Speech Recognition Review

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(More customer reviews)This book is a comprehensive and excellent introduction to the ever-expanding
field of Automatic Speech Recognition. Starting with models of speech
production, speech characterization, methods of analysis (transforms etc),
the authors go onto discuss pattern comparison, hidden Markov models (HMMs),
and design and implementation of speech recognition systems, right from
isolated word recognition to large vocabulary continuous speech recognition
systems. Neural networks and their use in speech recognition is also presented,
though somewhat briefly.
Rabiner was the author of the first widely-read tutorial on HMMs, so
naturally the presentation of HMMs is one of the strong points of this
textbook. The theory is developed in detail, but in an easy to follow
fashion, starting with the very basics and with plenty of helpful examples.
The implementation is discussed at great length as well, starting with
the simplest of tasks and progressing to the state-of-the-art (circa 1993).
That isn't to say that HMMs are the only good part of this book - indeed,
practically every topic, whether it be perception, transforms, vector quantization
or dynamic programming, is presented with great clarity. This book really is easy to
learn from, with numerous examples and illustrations.
The field of speech recognition is inherently multi-disciplinary in nature,
drawing upon various areas of study, including Physics, Physiology, Acoustics,
Signal Processing and Computer Science, to name but a few. The authors do a
great job of explaining all these facets, as well as the mathematics that
is an essential tool.

The only caveat is that it's now a little old (published 1993), since the
field has been growing by leaps and bounds - so while the basics remain
the same, things have changed and hence what's said here should not be
taken as the last word on the subject.
Perhaps a new edition is due, and would certainly be most welcome.
However, for an excellent, accessible introduction to this exciting field,
this is still a great choice.

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Provides a theoretically sound,technically accurate, and complete description of the basicknowledge and ideas that constitute a modern system forspeech recognition by machine. Coversproduction, perception, and acoustic-phoneticcharacterization of the speech signal; signal processing andanalysis methods for speech recognition; pattern comparisontechniques; speech recognition system design andimplementation; theory and implementation of hidden Markovmodels; speech recognition based on connected word models;large vocabulary continuous speech recognition; and task-oriented application of automatic speech recognition. For practicing engineers, scientists,linguists, and programmers interested in speech recognition.

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Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (The Morgan Kaufmann Series in Data Management Systems) Review

Average Reviews:

(More customer reviews)Witten and Frank have generated a book that is readable without eliminating all technical (yes, even mathematical!) descriptions of the key data mining algorithms. And they are up-to-date, including support vector machines and boosting. There are sufficient examples of the techniques to provide readers with a good feel for what each technique can accomplish. For example, how many books can provide a readable explanation of support vector machines?
There are some quibbles, such as not including any discussion of neural networks (noted in Ch. 1 with another reference)--I believe it deserves some attention because of its widespread use. Additionally, future editions should include a least a brief summary of data preprocessing, input selection, feature creation, etc. But these are quibbles.
The Java portion of the book is not of as much interest to me, but for those wishing to implement the algorithms, it provides a nice blueprint (from the code I looked at).
For what they have undertaken, they have performed admirably, and I would highly recommend this book.

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Neural Networks for Pattern Recognition Review

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(More customer reviews)This book came out at about the same time as Ripley's, which has almost the same title, but in reverse. At the time, I liked Ripley's better, because it covered more things that were totally new to me. Then a friend said he had chosen Bishop for a course he was teaching, and I went back and reconsidered the two books. I soon found that my friend was right: Bishop's book is better laid out for a course in that it starts at the beginning (well, not quite the beginning--you do need to be fairly sophisticated mathematically) and works up, while Ripley's is more a collection of insights all at the same level; confusing to learn from. Bishop is able to cover both theoretical and practical aspects well. There certainly are topics that aren't covered, but the ones that are there fit together nicely, are accurate and up to date, and are easy to understand. It has migrated from my bookcase to my desk, where it now stays, and I reach for it often.
To the reviewer who said "I was looking forward to a detailed insight into neural networks in this book. Instead, almost every page is plastered up with sigma notation", that's like saying about a book on music theory "Instead, almost every page is plastered with black-and-white ovals (some with sticks on the edge)." Or to the reviewer who complains this book is limited to the mathematical side of neural nets, that's like complaining about a cookbook on beef being limited to the carnivore side. If you want a non-technical overview, you can get that elsewhere (e.g. Michael Arbib's Handbook of Brain Theory and Neural Networks or Andy Clark's Connectionism in Context or Fausett's Fundamentals of Neural Networks), but if you want understanding of the techniques, you have to understand the math. Otherwise, there's no beef.

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Spoken Language Processing: A Guide to Theory, Algorithm and System Development Review

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(More customer reviews)This book is a comprehensive overview of most of the major topics associated with speech processing. Divided into five main sections, the book is well structured with a clear division of concerns. The title, "Spoken Language Processing", may be misleading to some as language processing topics only accounts for one section of the book.
The first two sections cover the fundamental theories that should be understood before embarking in-depth into a study of speech processing. This may seem an obvious approach but many texts do not follow this pattern making their use as reference tomes limited. Separating background theory from its use is also useful in that it allows a rigorous approach to its description. Too often texts give a hurried imprecise overview of theories used before launching into a long and complex use of the theory; losing the reader instantly in a quagmire of formulae.
The first two sections of the book deals with background material, material that the reader should at least understand the key concepts of. The first section concentrates on speech in general (including production and perception), probability and statistics, and pattern classification. These last two topics mentioned are both important parts of the book and are dealt with in their own chapters. Both are well written with the right amount of explanation and background. Much of the remainder of the book expects at least some familiarity with the material presented here. These chapters, like all chapters in the book finish with a section entitled, "Historical Perspective and Further Reading". The inclusion of recommended further reading, in addition to the vast number of references appearing in each chapter, make the book as a whole a very good starting point for any work in speech processing.
The second section concerns itself with the DSP topics which relate to speech processing. In this section the reader will find everything from FFTs to multi-rate signal processing and speech signal representations to speech coding. Again the section is well written and the reader is not forced to refer to other texts to understand what is written. If a topic is not expanded upon here then it is an indication that is not dealt further in any great depth in the remainder of the book.
The third section of the book covers speech recognition and is probably the section which will find most use with many readers. This section is very thorough in its treatment of the subject. It starts immediately with a discussion of Hidden Markov Models which is almost exclusively the method employed in the pattern matching stage of speech recognition. Any algorithms that are mentioned are also detailed which really make the book useful. In fact algorithms are presented throughout the book making it a practical reference as much as a theoretical one. This is important because there is a big jump from understanding theory to being able to implement an algorithm to exploit that theory. Other topics covered include an excellent chapter on environmental robustness with one of the best discussions of microphones I have seen. Language modelling and search algorithms are given a thorough treatment. I would like to have seen more detailed information on front-end processing and endpoint detection, as this remains a critical stage of the recognition process. Perhaps the level of detail reflects the fact that this is currently a hot research topic with potential for significant advancement.
Section four, on text-to-speech processing, is a good overview of the field and better than any book I've seen on the subject. It shows numerous block diagrams of what you need to build such a system and gives numerous algorithms in pseudocode. It also dedicates a subsection to each block of the text-to-speech system block diagram, discussing in detail what you would need to do to implement that particular block. Since much of the individual blocks have been discussed earlier in the book, it refers you back to specific earlier sections for details.
The fifth section is a short one on entire systems and shows some case studies, concentrating on what Microsoft was doing at the time this book was published, since that is where the authors' research came from. I would highly recommend that anyone anticipating getting into speech processing have a copy of this classic nearby.

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This will be the definitive book on spoken language systems written by the people at Microsoft Research who have developed the voic-activated technologies that will be imbedded in Windows 2000 and other key Microsoft products of the future. This is not a Microsoft book, however, this is a book on the science and linguistics of this technology and how to use it in developing and building hardware and software products.

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