Machine Learning Books

David Fogel has done a painstaking job of examining the historical record of Evolutionary Computation (EC) and recording both early and seminal papers in field. As a lecturer on EC, I have found the book to be an important, intriguing and insightful supplement to the course.

I think the book's strengths are twofold. First, that the important ideas in EC "popped up" in many earlier guises. I find it fascinating to discover concepts like "schema theory" and "bloated programs" addressed in at least a primitive form in papers going back to the 1950's. EC may be a "new science" but it clearly has deep roots. Second (and a more general point), that ideas themselves are not all that is required to do science. Timing and other factors play a role in how ideas get pushed forward and recognized by other researchers. It is a point that would be well taken by young researchers in any field.

There are some things that could be improved. One could quibble about the selection of papers, though I think Dr. Fogel's selections are well justified. For readability's sake I think the formatting of some of the papers could have been redone. Furthermore some papers might have been better presented in an abridged format. Overall, however, I think the book's minor flaws are far outweighed by its contribution to the field. Serious students in EC should definitely look at this book.

The collection of papers included in this book not only serves to explore the origins of evolutionary computation, but also shows some contributions that could had been turning points in the field but that somehow never received enough attention. The comments of David Fogel preceding each chapter are refreshing and show a deep and extensive knowledge of the field. His meticulous work of selecting, editing and commenting this valuable collection of papers certainly deserves my highest admiration. I have decided to use some of the papers contained in this book for my Graduate courses and seminars on evolutionary computation because I think that these early attempts (either successful or not) to simulate evolution in a computer must be studied by any serious EC researcher.

This is not your ordinary volume of collection of papers, this is a treasure chest for all those who truly want to understand the "evolution" of the ideas behind contemporary Evolutionary computation. David Fogel's thorough knowledge of the field and his passion for>tracking down the origins of the key ideas are evident in his introductions to each group of papers. Each time I have opened the book I have made delightful and often quite unexpected discoveries for myself. I wish to thank David Fogel for this outstanding work.

Evolutionary computation techniques (i.e., techniques based on the metaphor of natural evolution) constitute one of the most fascinating areas of computer science. Despite a long history of research spanning over several decades, evolutionary techniques are still of increasing interest because of their applicability to many real-world problems in science and engineering. However, many recent discoveries have their roots in the past (this is probably true in any discipline of science), and perforce, it is important to "look back" at some of the early developments in this field. Apart from the interesting ideas that emerged many years ago (e.g., artificial life, co-evolution, evolving computer programs, etc), a number of papers in this volume contain latent ideas that have not been fully exploited.

David Fogel accomplished a great feat by searching, reading, and selecting a collection of papers that constitute "the fossil record of evolutionary computation." This volume contains almost 30 important research articles that establish the foundations of evolutionary computation, including seminal articles written by Ingo Rechenberg, Lawrence Fogel, John Holland, Hans Bremermann, Nils Barricelli, Alex Fraser, Michael Conrad, and John Koza. All the articles were grouped carefully into meaningful units, each prefaced by an introduction written by David Fogel.

Researchers will find this volume to be an extremely interesting guide to the background of concepts of evolutionary computation. It is appropriate for anyone who is in search for such answers as: where did these techniques come from? where are they going? and what is their potential? But, above all, the book provides a unique experience of addressing the most fascinating question: "how is an idea born"? For this reason alone, this book is a must for any researcher in this or any other related field.

A rare piece of scholarship by one of the leading scientists in the field. Evolutionary Computation has only recently matured to the point of being a separate discipline. The Fossil Record is a remarkable compilation of foundational research. Fogel does an excellent job of placing each work in its historical context. In many cases, he was fortunate enough to interview these pioneers of computer science, artificial intelligence, mathematical biology, engineering, cybernetics, and evolutionary theory. As such, he provides unique insights into the motivations, methodologies, and philosophies of some of the most original thinkers in science.

Russell W. Anderson, Staff Scientist, HNC Software, and Associate Editor, IEEE Transactions on Evolutionary Computation

It is the best book on kernel methods. It covers a wide range of subjects.

The best thing is that after finishing one or two basic chapters, you can read the rest of the book in any order; most chapters are almost independent to each other. At the beginning of a chapter, the authors list the prerequistites, so a reader knows whether he will be able to understand the chapter.

For now the book still reflects the state of art. But it is a fast changing field. I hope the authors will update the book in the future.

Excellent theory on SVMs and VC dimensionality. However, I found the chapters on optimization a bit terse. Otherwise, an essential reference for those interested in using SVMs in classification and regression.

This book is at the right level if you are already strong in Machine Learning theory. (e.g. Tom Mitchell's "Machine Learning").

Note that it is already getting somewhat dated. It for example includes little information on kernels for discreate structured input, such as trees and graphs.

Great book, but a word of caution, it is not for the novice.
Book assumes a lot of background in functional analysis and
probability. True, it has extensive appendixes but they are
short-handing the relevant materials only. However, having said
that, this is a book worth struggling with even if you have not
yet got the intuitions in the above mentioned disciplines.

It is worthwhile (at least as I can tell) to read the book
skipping the tool chapters (2-6) going back to them when one has
a point where those are needed. I found that to be much easier
as it provides a concrete use of the methods putting them
in context.

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.
Advantage 1: Pattern recognition is a field of many disciplines. It has been studied by statisticians, mathematician, probabilists and engineering and people that call themselves computer scientists specializing in artificial intelligence. The field is old and has a long history but each discipline has developed their own jargon and many times the wheel has been reinvented. The advantage of this book is that these young scientists don't see that awful history. They have learned and mastered their subject in a basically engineering jargon but they include many concepts from statistics and statistical learning theory that are not common to engineering texts. This includes such topics as robust regression, ridge regression and spline estimation. Much of the classical statistical literature is cited. The book contains over 600 references including much of the authors own work.
Disadvantage 1: Because they are young they miss some of the important historical literature and key texts. I found it a little disappointing that the bootstrap which is a statistical tool that has played a major role in discriminant analysis (particularly in the estimation of classification error rates) was completely overlooked. Also although many important texts on pattern recognition, machine learning and discriminant analysis are cited the fine text by McLachlan is overlooked as is the recent relevant text by Hastie, Tibshirani and Friedman.

Advantage 2: This book highlights the work of Vapnik and Chervonenkis and provides nice concise descriptions that one can easily refer to when needed. The mathematics is deep and includes reproducing kernel Hilbert space and many important properties from functional analysis and statistical theory.

Disadvantage 2: The authors are more experienced at writing professional papers than at writing text books. Consequently the book does not flow well and the authors freely admit in their preface that it is best not to read the book in sequential order but rather to take the suggestions in the preface that differ based on the readers background and interest.

Having said all this, for someone like me, who is very knowledgeable about statistical pattern recognition this is a great text for getting me up to speed on an exciting new area that I know very little about. I became curious about it when I started reading Vapnik recently.

I am hoping that a careful reading of this book will give me an intuition about why this approach that incorporates kernel methods can be a powerful tool in pattern recognition and classification.

This book should be a useful reference for anyone interested in this research area. It could be used in an engineering or statistics course in pattern recognition at either the undergraduate or graduate levels depending on what material is covered.

In a recent communication with Bernhard Scholkopf I learned that his book was sent for publication before the Hastie et al. book went to press. So that is the only reason it wasn't referenced. I think that point is worth my mentioning in an editing of this review. Also on reflection I do not think the disadvantages are so great as to remove a star. So it is 5 stars for them.

I can only hope that they will reference the work of McLachlan and Hastie et al. in their future books and research on this subject.

I have been looking for this book and I have finally found it. I read this book when I was in the fourth grade and absolutely loved it. Now I am getting it for myself and my 10 year old nephew (does that date me or what?!?) I would highly recommend this book to anyone girl or boy. It's a great adventure story.

ISBN 0064402223 - Not the best thing I've read in kids' books lately, but good enough to hook some of the reluctant readers, especially boys.

Steve and Max are best friends, which is a good thing, because otherwise Max might have killed Steve by now. Who could get away with convincing you to eat a dog treat, if not your best friend? So when Steve comes to the clubhouse with a $2.50 time machine, Max isn't surprised. He doesn't take it seriously, but he's not surprised at all. After some explaining, he agrees to go with Steve to the year 1250... even if the contraption doesn't look like it's going to go anywhere. When Steve thinks Max is getting cold feet, he flips the switch before Max knows what's going on and they find themselves in the bodies of Sir Robert, a medieval knight, and his horse!

Stunned that it worked, thrilled with their success, the boys learn how things work in the Middle Ages. From quack doctors with potions to romance with an Earl's daughter, they're enjoying themselves quite a bit. Now all they have to do is hope the time machine brings them back before one of them is killed!

This is the kind of book that could easily translate into a series, with the boys travelling through time. Since the inventor of their machine, Professor Flybender, went off in search of Atlantis, never to return, there's a story to be told there - and if they tell it, I'll read it!

This is a fun, funny adventure book that no parent should pass up! The authors' quick wit, inside jokes and genuinely funny dialogue make this a great book to take turns reading aloud. The boys get themselves in and out of trouble with clever plans and a healthy sense of humor. Give it a try!

very funny easy read novel following two friends as they travel back in time to the middle ages where they find themselves in the bodies of people/animals who lived at the time.

I remember reading this book when I was young.. and I have read it several times since. It is fun, witty, charming and covers everything from time travel to jousting to courtly love. It's great for boys or girls! A definate must!

I received this book in High School as part of the "Electrical Installation" curricullum in January of 1986. At the advice of my instructor, Mr. Bergovoy, I didn't return the book at the end of the school term. I ended up having to pay for the replacement. However, this book has paid for itself thousands of times over the last 20 years. I recommend it to electricians ranging from the student to the master level..

This book is great. I've read many books on repairing electric motors , but none of them have given me a practical & theoretical view on the subject as this book has. I recommend this book to students, and fully quallified engineers. The best...... My greatest thanks to the authors.

started as a plant electrician and kept learning thanks to this book now repair light fixture to refrigeration

If your looking for a book that is all meat, if your looking for the "bible" of motors, if your desire for motor knoweledge is from the most basic to advance knoweledge.....YOU have to buy this book. I am an electrician, and industrial controls technician and I have found no better book. I am looking through my well used second edition, that was given to me while in college in 1984, and if you could see just my first page of the book, at how much highlighting and notes in the margins there are, you would see that nearly every sentence in the book is packed full of useful information,...IF... you take the time to read it. I believe it is as clearly written as you can get on a technical subject. I consider myself just a slightly above average student, and even I could understand this material. From casual interest in motors to engineer, this book needs to be on your technical bookshelf, unlike other technical books at this price that I threw out after college, this is a keeper. Check out all the other reviewers here, then buy the book.

I am an electrician whose job requires trouble shooting different types of equipment for different customers in many different environments. I see all kinds of motors and machines and jury rigs. This book is invaluable for troubleshooting any motor on the planet. It gives excellent reference schematics and diagrams that coincide with the different chapters. It has helped me on many occasions. It's really meant for poeple who
re-wind motors more than trouble shooters but you will not regret owning it if you do any kind of maintenance or trouble shooting on a variety of machines.

Langdon and Poli do a fantastic job of summarizing the major theoretical results of genetic programming. The first chapter gives a quick and clear introduction to genetic programming. They continue with a comprehensive summary of previous research in schema theory, and then they present their exciting theoretical results. Their description of an exact schema theorem (microscopic and macroscopic) for GP is a bit dense, but they provide a good discussion of how to interpret these results. As a whole, this book is generally easy to follow, even with little prior exposure to genetic programming. Of course, this book is not intended to be a general introduction to genetic programming (one of John Koza's books would be more appropriate), but instead it is intended to present some of the theoretical foundations of the field.

This book was published in 2002 to provide a survey of the direction research had taken in the field of Genetic Programming. There is an explanation of what genetic programming is and how it is different from genetic algorithms in chapter 1(GP is a "generalization" of GA). Chapter 2 discusses the problems with the fitness landscape. Chapter 3 - 6 discusses various schema theory approaches and proofs. Chapter 6 has a great explanation of effective fitness.

There are numerous theorems and proofs in the book. There are informative examples of the max problem and the artificial ant (Santa Fe Trail) problems. Chapter 11 is about how GP convergences are a tricky matter and how subtrees can hide interesting incidences of convergence.

This is not an introductory text, it is intended for graduate level or higher readers. There is much theoretical work here and a limited background in this area will result in limited understanding of the material.

Langdon and Poli are both internationally recognized experts in Evolutionary Computation (EC) and, in particular, Genetic Programming. They have both contributed extensively to the theoretical "foundations" of GP and hence may speak with no small degree of authority about GP theory. As a physicist working in EC I like the balance that the authors have struck between mathematical rigor and understandable intuition. The book is not as rigorous as Vose's well known GA book. However, it is much easier to read. Neither does it take the "engineering" rule of thumb approach, as does Goldberg's book for instance. It covers very well recent important developments in the theory of GP and in that sense makes very good reading for anyone with a serious interest in EC theory. It is not for the novice, even though technically it is not a difficult book. It is really a research monograph and not a textbook. In that sense the title is a little bit misplaced. With the exciting direction the authors are pointing in I believe that in five years time another book of the same title should truly be able to lay out what are the foundations of GP theory and also show the theoretical unity that exists between the different branches of EC.

This book can be usefully read along with a companion text by the same publisher - "Introduction to Evolutionary Computing". Langdon and Poli provide a focused look, on the specifics of genetic programming. The maths treatment here is significantly more involved than the other book.

Foundations starts with what I suppose in this field is an obligatory section on the concept of a fitness landscape. A very useful metaphor of what you'll be attempting to do, as a researcher. However, the authors carefully point out the limitations of this idea. Notably that some spaces might have no natural metric.

The book then rapidly goes into the ideas of GP schemas and hyperschemas. Accompanied by a nice theoretical analysis of key performance goals like the rate of convergence in the GP search space. A solid offering to the GP researcher.

Currently working as an undergraduate student in Ann Arbor, Michigan as a Computer Science major I'm an intrigued by Genetic Programming alongside all motives of this in-depth field. I found this book to be a modest account of what is new and theoretical within this field. Expressing advanced features with a short introduction; this book is profoundly for somebody with somewhat of a background. A recommended start in the computer evolutionary field is:
An Introduction to Genetic Algorithms [1996], by Melanie Mitchell.

Vapnik and Chernovenkis extended the Glivenko-Cantelli Theorem in their work on classification and statistical learning. Vapnik in recent texts has described a form of nonparametric statistical inference based on approximating functions and the Vapnik-Chernovenkis dimension.
In an earlier book published by Springer-Verlag he develops the basics of the theory. However to keep the mathematical level excessible to computer scientists and engineers he avoided the mathematical proofs needed for mathematical rigor. This text is an advanced text that provides the rigorous development. Although the preface and chapter 0 give the reader a idea of what is to come the rest of the text is difficult reading.

The theory has been quite successful at attacking the pattern recognition/ classification problem and provides a basis for understanding support vector machines. However Vapnik sees a much broader application to statistical inference in general when the classical parametric approach fails.

If you have a strong background in probability theory you should be able to wade through the book and get something out of it. If not I recommend reading section 7.9 of "The Elements of Statistical Learning" by Hastie, Tibshirani and Friedman. That will give you an easily understandable view of the VC dimension. Also sections 12.2 and 12.3 of their text will give you some appreciation for support vector machines and the error rate bounds obtainable for them based on the VC dimension.

This book aims at rigorours and deep treatment of statistical learning and is divided into three parts :

(I)THEORY OF LEARNING AND GENERALIZATION;

(II)SUPPORT VECTOR ESTIMATION OF FUNCTIONS;

(III)STATISTICAL FOUNDATION OF LEARNING THEORY'

For anyone intending to dive into this topic intriguing readers shull find their task rather not simple when exploring this mathematical exposition.This is because of the mature nature behind the basic theory .In order to gain most of the benefit ,interested and even involved researchers are urged and should assume all the requirements for a vast and solid mathematical background.

I Think the book constitutes a respectful and organized 'exhibition' that you will not find in any other place. Althought there are excellent books discussing SVMs and Machine-Learning/ Intelligence,eventually all emenate from the theory.Regarding the book rating it is was not rated upon how much you retrieve as concepts, but how well the propositions offer a precious appreciation of the substantial theory.In otherwords, this book is not the place for a first time learning, but it is serves as a bridge between interrelated elements of such incredibly growing area.

For the book: "The Nature of Statistical learning Theory" also by Vapnik you can find a review by Vladimir Cherkassky in The IEEE TRANSACTIONS ON NEURAL NETWORKS VOL. 8, NO. 6, NOVEMBER 1997 .

The field of statistical learning theory has not only seen considerable advances in the last fifteen years, it has also found many applications, some of these appearing in commercial packages. It is now classified as a subfield of artificial intelligence, and as such gives an alternative, and frequently more general viewpoint on such topics as pattern recognition, regression estimation, and signal processing. The author of this book is one of the originators of statistical learning theory, and has written a book that will give the mathematically sophisticated reader a rigorous account of the subject. Most of the main results are proven in detail, but the author does find time to include insightful discussion on the origins and intuition behind the concepts involved in statistical learning theory.

Along with a brief introduction, the book consists of three parts, the first being an overview of the statistical theory of learning, the second giving the details of the now widely used support vector machines, and the last one (the most sophisticated mathematically) giving the statistical foundations of learning theory. In writing the book, the author wants to put forward a new approach to dependency estimation problems having their origin in learning theory, and being able to deal with the ?curse of dimensionality?. The origins of the subject lie in the pattern recognition problem and the Glivenko-Cantelli problem in statistics. Both of these problems were discovered to be essentially the same, and the author?s task is to use their similarities to construct a general theory of statistical inference and (inductive) learning. Indeed, a new induction principle, called ?structural risk minimization? (SRM) is paradigmatic in the book, along with the now ubiquitous VC dimension, the latter of which originates in the author?s early research. Both the SRM and the VC dimension illustrate the tension between the need for high accuracy and the need for the minimization of error in data sets.

The learning problem, as the author sees it, is the problem of selecting the correct dependence on the basis of empirical data. Two approaches to this problem are discussed, the first using a ?risk functional?, and the second involving the estimation of stochastic dependencies and the consequent solution of integral solutions. Both of these approaches are modeled in terms of a general model of learning from examples, which consists of a data generator, a supervisor, and a learning machine. The learning machine can either imitate the supervisor or identify how the supervisor operates. These two methods are different, the author says, in that the first one searches for the best prediction based on the data, while the second one attempts to approximate the operator representing the supervisor. Both approaches are studied in the book, with the first one being the easier of the two, while the second involving the solution of ill-posed problems. The author views the learning process in terms of choosing the right function from a given function collection.

Both perceptrons and their generalizations, neural networks, are briefly discussed in the book, along with the back-propagation method. The author gives reasons why he does not think neural networks are well-controlled learning machines, such as the existence of local minima, the slow convergence of the gradient method, and the choice of scaling factors. These problems serve as motivation for the introduction of support vector machines, which are introduced as optimal separating hyperplanes. Support vector machines take input vectors into a high-dimensional feature space via a nonlinear mapping, and an optimal separating hyperplane is then constructed in this feature space.

Similar to the need for neural networks to generalize well, separating hyperplanes must do the same, and due to the large dimensionality of the feature space, a hyperplane that separates the training data may not generalize well. In addition, the large dimensionality of the feature space makes the construction of the hyperplane computationally demanding. The author shows that optimal hyperplanes, found using various mathematical techniques such as quadratic optimization, do generalize well. Also, as the author points out, the explicit form of the feature space need not be known, since only the inner products between the ?support vectors? and the vectors of the feature space need to be calculated. The calculation of the inner product is done with the insight gained from Mercer?s theorem, which gives the existence of a kernel function such that there exists a feature space where this function generates the inner product. This inner product in feature space allows the construction of a decision function that is nonlinear in the input space but that is equivalent to a linear function in the feature space. Different choices of the kernel function give different types of learning machines. The author discusses three examples of support vector machines for pattern recognition: polynomial, radial basis function, and two-layer neural network support vector machines. An entire chapter is spent on the problem of digit recognition using support vector machines.

Vapnik and Chernovenkis extended the Glivenko-Cantelli Theorem in their work on classification and statistical learning. Vapnik in recent texts has described a form of nonparametric statistical inference based on approximating functions and the Vapnik-Chernovenkis dimension.

In an earlier book published by Springer-Verlag he develops the basics of the theory. However to keep the mathematical level excessible to computer scientists and engineers he avoided the mathematical proofs needed for mathematical rigor. This text is an advanced text that provides the rigorous development. Although the preface and chapter 0 give the reader a idea of what is to come the rest of the text is difficult reading.

The theory has been quite successful at attacking the pattern recognition/ classification problem and provides a basis for understanding support vector machines. However Vapnik sees a much broader application to statistical inference in general when the classical parametric approach fails.

If you have a strong background in probability theory you should be able to wade through the book and get something out of it. If not I recommend reading section 7.9 of "The Elements of Statistical Learning" by Hastie, Tibshirani and Friedman. That will give you an easily understandable view of the VC dimension. Also sections 12.2 and 12.3 of their text will give you some appreciation for support vector machines and the error rate bounds obtainable for them based on the VC dimension.

Text mining is one of the most exciting subjects of the web, and too few books are dealing with it. This one is one of them, and it gives quite a few examples of text mining applications, like spam filters or search engine ranking algorithms. The style is easy to follow, and the concepts easy to understand given some maths background.

However, I expected more details, and a richer content overall, thus the four stars. This is still a good book.

Software developers learn how to mine information on the Web and turn it into valuable data; but developers need to understand how data mining works. For a programmer's application-oriented review, Text Mining Application Programming is the item of choice: it reviews text data, how it's found, and how search engines locate and gather it. Next, it teaches how to build spiders to crawl the Web, how to use the information, and how to monitoring it. Perl developers will find its Perl-based code useful, but it's not necessary to know Perl to run the software herein. An excellent guide to mining the Net.

There is an old expression that half of knowing anything is knowing where to find it. And there is little more frustrating to be looking at 'My Computer' trying to find what you know you have stored in a file somewhere. Well, perhaps just as frustrating is to go to one of the search engines and try to find something that you know is there but just don't know the proper words to find it.

In this book Dr. Konchady talks about how to go find data that is in text form on your system, on your network or out on the web somewhere. It talks about search engines, but also about other techniques that can be used only by programming.

The CD that comes with the book contains several Perl software snippets that help to find named entities, parts of speech, phrases and gives a summary of text documents. This area includes developing web crawlers that can be adapted by individual users to go out and find specialized information. It further contains an Open Source software package called Text Mine that is designed for mining operations. In addition it has utilities to build and enhance Text Mine and utilities to build and manage MySQL database tables. This is an excellent book on everything from the basic hints and types through some of the mathematics that underlies text mining.

His section on the nature of an English language Question and Answer system is the best I've ever seen.

Our consulting firm has successfully collaborated with Dr. Powell for years and I have seen first hand how ADP solves large scale, real world problems that would frankly be intractable by many traditional traditional operations research or optimization techniques. While consulting firms and other business jealously guard their intellectual property, it is terrific for all of us that academics are rewarded for precisely the opposite. I would highly recommend for any serious practitioner to grab a copy of this book and study it. Probably one of the best $100s you will have spent in a while.

This book represents a paradigm shift in the presentation of dynamic programming/stochastic optimization. Classical treatments of dynamic programming/neuro-dynamic programming/reinforcement learning typically assume small "action spaces," and often assume the presence of a one-step transition matrix. By contrast, authors working with decision vectors in the presence of uncertainty often turn to stochastic linear programming. But these techniques typically struggle when applied to multistage applications. It is extremely hard to solve most of these problems without taking advantage of the presence of a state variable that captures previous history.

I have adopted the notational style where S is the state of the system, and x is a decision, using the language of math programming. x may have many thousands of dimensions for some problem classes (although the book considers many classical problems where decisions are relatively simple).

The challenge that arises when x is a vector when we use dynamic programming is the expectation within the max/min operator. Bellman's equation is typically written

V(S_t) = max (C(S_t,x) + discount * E{V(S_{t+1})|S_t} )

If x is a vector, we generally need the power of math programming to solve the maximization problem. The challenge is the expectation. We avoid this using the post-decision state variable, which is the state immediately after we have made a decision, but before any time has passed (bringing new information). Denoted S^x_t, the post-decision state variable is a deterministic function of S and x. If V^x(S^x_t) is the value function around the post-decision state variable, we obtain

V(S_t) = max (C(S_t,x) + discount * V^x(S^x_t)

The book provides a number of practical examples of this, but the key is that the maximization problem is now a deterministic problem. The final step is that we have to replace V^x() with a suitably chosen approximation. If our maximization problem is a linear, nonlinear or integer programming problem, we have to choose an approximation for V^x() that allows these algorithmic tools to be used.

In this book Warren nicely blends his practical experience in modeling and solving complex dynamic and stochastic problems occurring in a variety of industries (transportation, the financial sector, energy, etc) with algorithmical and theoretical aspects of approximate dynamic programming. The book can be either used as a textbook in undergraduate or graduate courses, or for practitioners to learn about recent advances in this exciting area. Indeed, I have already used it twice as a textbook for a graduate course, and on the other hand, I have recommended it to several practitioners. Without doubt, this is an important contribution in approximate dynamic programming.

I strongly recommend the book for all practitioners facing large-scale complex dynamic programs. It is also an excellent textbook.

This is a book by Springer Verlag that came out if 1999. This book introduces a lot of useful statistical tools and has chapters written by statisticians and computer scientists. The editors also contribute. They emphasize useful tools and computer tools. It includes material from the artificial intelligence literature including fuzzy set logic, genetic algorithms and expert systems. There is some discussion of data mining, Bayesian methods and neural networks.

Chapters are written on an elementary level for students and pratictioners of modern data analysis techniques. Written mainly as a text but expanded to cover topics of interest to researchers in statistics and computer science by subject matter experts. The last chapter on Systems and Applications by Xiaohui Liu includes coverage of data quality. Among the references on data quality and outlier detection is the book edited by Wright "Statistical Methods and the Improvement of Data Quality". That book was a collection of papers from a conference held in Oak Ridge Tennessee in 1982. That volume was published by Academic Press in 1983. It is not often sighted in the statistical literature but it did contain a number of interesting papers. I contributed a chapter on influence function methods for outlier detection to the Academic Press book.

Hand has written many books on statistics and especially some excellent texts on classification and pattern recognition. His recent work on data mining was published in 1999 by MIT press, a volume he coauthored with Mannila and Smyth. it is one of teh few data mining texts that is highly regarded by the statistical community. Much of that work in referenced in this book particularly in Chapter 1, the overview chapter on intellegent data analysis that Hand wrote himself.

Resampling methods, generalized linear models, Bayesian methods, time series, multivariate analysis, random effects models and entropy are all covered with nice elementary introductions.

This is a great reference source with over 440 articles and books in the list of references.

This book provides a detailed presentation of several important approaches to intelligent data analysis. It has ten chapters, each chapter written by a different technical specialist. The book could well serve as a text for a graduate level course on data analysis. It also works well as a reference. There are many useful illustrations and examples.

The first part of this book is focused on classical statistical issues. Arguably, anyone seeking to perform advanced data analysis should have a working knowledge of this area. It is my personal observation that, unfortunately, many workers do not. This book provides a good way of gaining a broad understanding of statistical methods. My only caveat is that the discussion of naïve Bayesian classifiers could have been more extensive. (The chapter on general Bayesian classifiers is other wise well done.) Naïve Bayesian classifiers have been reasonably successful in machine learning and a more in depth treatment would have been useful.

The later chapters focus on machine learning. They provide useful introductions into: induction, neural networks, fuzzy logic, and stochastic search. These chapters are particularly useful to workers contemplating how to best perform advanced analysis of complex, large, and possibly imprecise data sets. Consequently, someone contemplating data mining or other intelligent data analysis applications should seriously consider acquiring this book.

This is a book by Springer Verlag that came out if 1999. This book introduces a lot of useful statistical tools and has chapters written by statisticians and computer scientists. The editors also contribute. They emphasize useful tools and computer tools. It includes material from the artificial intelligence literature including fuzzy set logic, genetic algorithms and expert systems. There is some discussion of data mining, Bayesian methods and neural networks.

Chapters are written on an elementary level for students and pratictioners of modern data analysis techniques. Written mainly as a text but expanded to cover topics of interest to researchers in statistics and computer science by subject matter experts. The last chapter on Systems and Applications by Xiaohui Liu includes coverage of data quality. Among the references on data quality and outlier detection is the book edited by Wright "Statistical Methods and the Improvement of Data Quality". That book was a collection of papers from a conference held in Oak Ridge Tennessee in 1982. That volume was published by Academic Press in 1983. It is not often sighted in the statistical literature but it did contain a number of interesting papers. I contributed a chapter on influence function methods for outlier detection to the Academic Press book.

Hand has written many books on statistics and especially some excellent texts on classification and pattern recognition. His recent work on data mining was published in 1999 by MIT press, a volume he coauthored with Mannila and Smyth. it is one of teh few data mining texts that is highly regarded by the statistical community. Much of that work in referenced in this book particularly in Chapter 1, the overview chapter on intellegent data analysis that Hand wrote himself.

Resampling methods, generalized linear models, Bayesian methods, time series, multivariate analysis, random effects models and entropy are all covered with nice elementary introductions.

This is a great reference source with over 440 articles and books in the list of references.

This is a hot book! I coached a team of manufacturing managers who worked in a large traditional factory. Our job was to study manufacturing operations in companies that had adopted Toyota's productivity methods and policies. While the men and women on the team had read about lean production, they were disquieted and perhaps even disturbed by obviously highly performing plants that were organized and operated according to principles foreign to their beliefs. At each plant we visited their discomfort deepened. Then, somewhere between the second and fourth visit, each manager had an epiphany. There was some kind of logical reorganization of the manufacturing furniture in their minds and they "got it", as they described the event. Others said, "the light came on." They saw the fundamental logic and sense underlying each lean factory even though each facility assembled pieces of Toyota's productivity methods and policies into its own unique manufacturing system. Interestingly, each member of the visit team became a passionate believer of lean manufacturing. The greatest skeptics became the most outspoken advocates. They called it "getting religion."

People who successfully implement lean manufacturing must be strong believers and must have a personal mental model of lean that functions at the level of a craft - a creative skill for assembling productivity methods and policies into powerfully efficient manufacturing machines. As the great Japanese coaches from Toyota teach Westerners, there is no cookbook, lean is a way of thinking.

The literature on lean production is disappointing. Lean manufacturing books tend to be long dreary laundry lists of productivity methods and technical techniques for quality. There is little available that gives insight into how the great master craftsmen and craftswomen put together marvelous lean machines of production - until now.

This book by Richard McCormack finally brings us face to face with the creative processes of great designers of production systems. Imagine yourself as a novice artist sitting down for a conversation with Auguste Renoir, Vincent Van Gogh, Toulouse-Lautrec or Michelangelo. That is what McCormack brings us in this book - chats with the virtuosos of lean production. Forget those paint-by-numbers books. Either go see the real thing or read "Lean Machines".

A lot has been written about lean, but nothing yet compares to what this book has done.... It's the first time anyone has provided straight answers about the true nature of lean. The author asks the right questions and gets surprising responses. Having spent 20 years in the automotive business, I found this book extremely useful.

This is a hot book! I coached a team of manufacturing managers who worked in a large traditional factory. Our job was to study manufacturing operations in companies that had adopted Toyota's productivity methods and policies. While the men and women on the team had read about lean production, they were disquieted and perhaps even disturbed by obviously highly performing plants that were organized and operated according to principles foreign to their beliefs. At each plant we visited their discomfort deepened. Then, somewhere between the second and fourth visit, each manager had an epiphany. There was some kind of logical reorganization of the manufacturing furniture in their minds and they "got it", as they described the event. Others said, "the light came on." They saw the fundamental logic and sense underlying each lean factory even though each facility assembled pieces of Toyota's productivity methods and policies into its own unique manufacturing system. Interestingly, each member of the visit team became a passionate believer of lean manufacturing. The greatest skeptics became the most outspoken advocates. They called it "getting religion."

People who successfully implement lean manufacturing must be strong believers and must have a personal mental model of lean that functions at the level of a craft - a creative skill for assembling productivity methods and policies into powerfully efficient manufacturing machines. As the great Japanese coaches from Toyota teach Westerners, there is no cookbook, lean is a way of thinking.

The literature on lean production is disappointing. Lean manufacturing books tend to be long dreary laundry lists of productivity methods and technical techniques for quality. There is little available that gives insight into how the great master craftsmen and craftswomen put together marvelous lean machines of production - until now.

This book by Richard McCormack finally brings us face to face with the creative processes of great designers of production systems. Imagine yourself as a novice artist sitting down for a conversation with Auguste Renoir, Vincent Van Gogh, Toulouse-Lautrec or Michelangelo. That is what McCormack brings us in this book - chats with the virtuosos of lean production. Forget those paint-by-numbers books. Either go see the real thing or read "Lean Machines".