Bayesian Artificial Intelligence (Chapman & Hall/Crc Computer Science and Data Analysis)

Book Description

With Bayesian network technology very much on the up-swing in industry and government, there is an increasing need for an introductory book that jointly emphasizes the understanding of its underlying priniciples and their application in practice. Bayesian Artificial Intelligence presents elements of Bayesian network technology, automated causal discovery, and learning probabilities from data along with extensive motivational examples of using these technologies to develop probabilistic expert systems. This practical, very accessible introduction balances the causal discovery of networks with the Bayesian inference procedures that use a network once it is found. The authors emphasize understanding and intuition, so they keep the mathematical details to a minimum, but also provide the algorithms and technical background needed for applications. They illustrate at length a number of applications and discuss application software in detail. A broad range of topics, a practical perspective, and a thoughtful discussion of philosophical underpinnings make Bayesian Artificial Intelligence an ideal introduction for students and for professionals who want to broaden their expertise. It provides the knowledge you need to put Bayesian network tools into practice, while also forming the basis for a more detailed investigation of the technology and original research.

Customer Reviews:

Very good introduction in causal Modeling.......2006-03-09

The book by Korb and Nicholson is very readable and structured. Starting with some background information in statistics it comes directly to the major topic of the book - bayesian networks. The theory thereof is nicely evolved and applied to small examples to demonstrate its usage. Each chapter finishes with a short summary and bibliographical notes for further readings.

In my opinion this book is well written and the chosen examples are insightful. What I do not like is part three of the book which is devoted to case studies and praktical examples. If this space had been used for the first two parts by providing more details, e.g., for the discussion of path models (which is given but only short), this book could be even great on a more advanced level. In this form it is very good as an introduction in Bayesian Networks and related topics like the larger class of causal models.

Excellent Introductory Text.......2004-12-17

It is difficult to assess a review without understanding the biases of the reviewer. I fall under the category of researcher/practitioner when it comes to reasoning with graphical models. I am familiar with and make use of several books and papers on this topic in my work. Of the set of standard references (Pearl, Jensen, Neapolitan, Jordan, Cowell et al., Borgelt & Kruse) the text by Korb and Nicholson (K&N) stands out in terms of its clarity and accessibility. Does the book have everything one would ever want to know about Bayesian inference? Not by a long shot. Is it, however, a good place to start? Definitely. The basic concepts are presented relatively completely and with clarity. I consistently recommend K&N over other alternatives to colleagues new to the field. Is there a chasm separating concept and algorithm in the book? I don't think there is, especially relative to other references. With tools such as Kevin Murphy's BNT, or Netica available on the Web, it seems to me that providing a solid conceptual framework becomes paramount for a textbook such as this. I believe K&N succeed admirably in this sense. Why four stars and not five? Even for an introductory text such as K&N, it would be nice to have more development of some concepts such as causality, context specific independence, or loss of independence in dynamic nets. Although it won't be your last book on reasoning with graphical models, K&N should probably be your first.

Bayesian Networks for Undergrads and Practicioners.......2004-01-12

Despite its name "Bayesian Artificial Intelligence" covers Bayesian network (BN) techniques only. Other Bayesian techniques useful for AI are not treated.
The content is divided in three main sections: (1) The basics of probabilistic reasoning with BNs, (2) Causal discovery (finding BNs from data), and (3) "Knowledge engineering".
The first part covers the fundamental concepts and algorithms around BNs and (simple) decision networks. It is well-written and clear, but readers who are not totally new to the field might find only little new information (e.g., loopy belief propagation, continuous densities, large decision networks, etc. are not covered).
The second part is on how to deduce causal relationships from observational data. Constrained-based and Bayesian approaches are covered, but on a rather general level. I am not sure how easy it is to implement the algorithms from the descriptions provided. When it comes to details of the algorithms, proofs, or mathematical background the authors very often refer to the literature due to "lack of space". From a practical standpoint, it is unfortunate that the different methods are compared to each other only superfiscially. For instance, one method presented performs a large number of statistical tests; one would expect that this requires large amounts of data in order to avoid false positive results. Is this a problem? With questions like these the reader is often left alone.
I am not competent to talk about part three (knowledge engineering), so I end with my general impression of the book: I would have appreciated if the authors had treated some the algorithms in greater detail and had spent a few pages on advanced concepts and current research directions. On the other hand, some information provided could have easily been left out. (For instance, how to download and install certain software packages from the internet, Kevin Murphy's well-known survey on BN software packages, screenshots of user dialogs, etc. just eat pages. Providing the URLs to the corresponding sites on the internet is completely sufficient, and the information there is more likely to be up-to-date.) The saved pages could then be spent on information which is not readily available elsewhere.
To summarize: The book provides a mostly well-written general overview of the basic concepts and could serve as a first introduction to the field. However, it leaves the reader often alone when it comes to the mathematical background, potential practical pittfalls, or advanced algorithms.

Bayesian Networks and Decision Graphs (Information Science and Statistics)

Average customer rating:

Good Book
A very good introduction to Bayesian networks
A lot about very little
Accessible introduction to Bayesian Networks
Not worth the money

Bayesian Networks and Decision Graphs (Information Science and Statistics)
Finn V. Jensen , and Thomas D. Nielsen
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

General | Graphic Design | Computers & Internet | Subjects | Books

General | Certification Central | Computers & Internet | Subjects | Books

General | Computers & Internet | Subjects | Books

General | Science | Subjects | Books

General | Mathematics | Science | Subjects | Books

Look Inside Computer Books | Trip | Specialty Stores | Books

Look Inside Science Books | Trip | Specialty Stores | Books

All Titles | Qualifying Textbooks - Fall 2007 | Stores | Books
Similar Items:

Accessories:

ASIN: 0387682813

Book Description

Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis.

The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models.

The book is a new edition of Bayesian Networks and Decision Graphs by Finn V. Jensen. The new edition is structured into two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems. The authors also

provide a well-founded practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams (and variants hereof), and Markov decision processes.

give practical advice on the construction of Bayesian networks, decision trees, and influence diagrams from domain knowledge.

give several examples and exercises exploiting computer systems for dealing with Bayesian networks and decision graphs.

present a thorough introduction to state-of-the-art solution and analysis algorithms.

The book is intended as a textbook, but it can also be used for self-study and as a reference book.

Customer Reviews:

Good Book.......2006-03-01

For an introduction to the subject, this book is unequivocal in my experience with the literature. Great read that has propelled me forward into combining a bayesian network with a physical model to approach a very complex sediment transport problem.

A very good introduction to Bayesian networks.......2003-06-15

I am very pleased to have found a book that gives a modern, sound, and self-contained introduction to Bayesian networks. The only prerequisite is basic knowledge of probability. This makes sense because a Bayesian network is essentially a directed graph whose vertex set is a collection of random variables, while an edge from one variable X to another variable Y represents a belief that X has a causative effect on Y. For example, X could be the pregnancy status of a cow, while Y could be a blood test administered to the cow. Vertex Y would contain a contingency table that reflects the conditional probability of Y in terms of X. The author does well in explaining this, as well as adequately treating many of the practical issues surrounding Bayesian networks, such as design issues, network learing and tuning, and some basic algorithms (e.g. bucket elimination and junction trees) that aid in the efficient updating of variable probabilities due to new evidence that may instantiate or change the distribution of one or more variables.
The author also provides a good introduction to decision graphs, a close relative of Bayesian networks.

The aspect of Bayesian networks that I find most attractive is the fact that there is a "rational" way of designing a network, based on hypothesis, informational, and mediating variables, and their "causal" relationships. Unlike neural networks in which one is almost forced to guess the appropriate structure of the network, every node in a Bayesian network correpsonds with a state or quantity that can be measured either directly or indirectly through other variables. Thus, changes in a system model should only induce local changes in a Bayesian network, where as system changes might require the design and training of an entirely new neural network.

Another aspect of Bayesian networks that I find very compelling is the way in which they seem quite amendable to learning and the presentation of new evidence. This is true since knowledge updating is done locally (through variables), while the effects of those changes are witnessed globally through appropriate belief-updating algorithms.

On the downside, it should be noted that the operation of belief-updating is in general NP-hard, thus there exists a valid concern about the computational efficiency of Bayesian networks. Contrast this with the fact that once a nueral network has been trained, it is quite easy to compute. One would hope that these concerns will subside with more research, for the above mentioned benefits of Bayesian networks leads me to believe that these networks will have quite an influence on the future directions of machine learning.

Although this book will not go down in history as the definitive reference for Bayesian networks, it serves as a good conduit for explaining this quite interesting area of learning at a time when such few complete and modern references exist.

A lot about very little.......2003-05-06

The book covers many topics, but doesn't really cover them well. I would not recommend this book. I have learned litte from it.

Accessible introduction to Bayesian Networks.......2003-01-21

Among currently available introduction to Bayesian networks (also known as Bayes Net, Bayesian Belief Nets), this book is probably one of the most accessible. The book is divided into part I and II. Part I is intended for BN users (practitioners) and Part II more towards BN developers and researchers, as it contains algorithmic introduction of BN.

Prerequisites of the book as stated in the preface include Graph Theory and Calculus, both at introductory level. I personally did not have exposure to Graph theory, but I was able to understand most of the material without any help. Necessary probability theory is developed, but basic probability knowledge is also a prerequisite to digest the material to a reader without prior exposure of Probability as it shapes the core of the material in the book.

The strength of this text is in Part I where the author provides several examples to illustrate use of Bayesian Networks, Influence Diagrams and other models. I find it useful Influence Diagram as an extension of Bayesian Networks.

Most answers to Exercises at the end of each chapter are provided at the author's homepage, except answers of the last chapter. Answers that require graphical modeling software are also provided in Hugin format. (Hugin Lite can be downloaded from Hugin site.)

The downsides are that writing of the text is somewhat awkward, obscuring readers from understanding, that model building chapter could have been discussed more thoroughly, that material in Learning is barely present, and that definitions are sometimes not introduced upon the first encounter but they appear later in chapters. More different and complex examples could have been discussed to illustrate the material. Note: the author provides a page for Learning at his homepage.

Although this is an introduction to Bayesian Networks and Influence Diagrams, a reader should be equipped with some level of abstract thinking in order to digest the material.

This book is suitable for self-study. It has motivations for the uninitiated. References are provided at the end of the book and I was able to find some of them online. A notable is "A tutorial on Learning with Bayesian Networks" by Heckerman, to fill in the part of Learning in this book.

Other books at this level from users' perspective are:
Edwards, Introduction to Graphical Modeling (Utilizes software MIM.)
Clemen, et al., Making Hard Decisions (Uses Palisade Decision Tools suite. The book discusses Influence Diagrams but not Bayesian Networks.)

Further studies after completion of this book include:
Cowell, et al., Probabilistic Networks and Expert Systems
Lauritzen, Graphical Models
Pearl, Probabilistic Reasoning in Intelligent Systems
Pearl, Causality

Not worth the money.......2002-12-31

Chapter 1 is a nice introduction to probability. Chapter 2 is readable. Chapter 3 is poorly presented, and you feel sad for having wasted so much money on a book with only one intelligible chapter.

Average customer rating:

Statistical Mechanics of Learning
A. Engel , and C. Van den Broeck
Manufacturer: Cambridge University Press
ProductGroup: Book
Binding: Paperback

General | Computers & Internet | Subjects | Books

General | Education | Nonfiction | Subjects | Books

General | Physics | Science | Subjects | Books

General | Education | Professional & Technical | Subjects | Books

Look Inside Computer Books | Trip | Specialty Stores | Books

All Titles | Qualifying Textbooks - Fall 2007 | Stores | Books
ASIN: 0521774799

Book Description

The effort to build machines that are able to learn and undertake tasks such as datamining, image processing and pattern recognition has led to the development of artificial neural networks in which learning from examples may be described and understood. The contribution to this subject made over the past decade by researchers applying the techniques of statistical mechanics is the subject of this book. The authors provide a coherent account of various important concepts and techniques that are currently only found scattered in papers, supplement this with background material in mathematics and physics, and include many examples and exercises.

Download Description

Learning is one of the things that humans do naturally, and it has always been a challenge for us to understand the process. Nowadays this challenge has another dimension as we try to build machines that are able to learn and to undertake tasks such as datamining, image processing and pattern recognition. We can formulate a simple framework, artificial neural networks, in which learning from examples may be described and understood. The contribution to this subject made over the last decade by researchers applying the techniques of statistical mechanics is the subject of this book. The authors provide a coherent account of various important concepts and techniques that are currently only found scattered in papers, supplement this with background material in mathematics and physics and include many examples and exercises to make a book that can be used with courses, or for self-teaching, or as a handy reference.

Average customer rating:

Least Squares Support Vector Machines
Johan A K Suykens , Tony Van Gestel , Jos De Brabanter , Bart De Moor , and Joos Vandewalle
Manufacturer: World Scientific Publishing Company
ProductGroup: Book
Binding: Hardcover

General | Computers & Internet | Subjects | Books

General | Software | Computers & Internet | Subjects | Books

General | Science | Subjects | Books

All Titles | Qualifying Textbooks - Fall 2007 | Stores | Books

Computers & Internet | Qualifying Textbooks - Fall 2007 | Stores | Books

Professional | Qualifying Textbooks - Fall 2007 | Stores | Books

Science | Qualifying Textbooks - Fall 2007 | Stores | Books
Similar Items:

ASIN: 9812381511

The Nature of Statistical Learning Theory

Average customer rating:

New to Field of Learning Theory
worth reading
A very nice book to get ideas on support vector machines
A research field described by the man who invented it

The Nature of Statistical Learning Theory
Vladimir N. Vapnik
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

Information Theory | Computer Science | Computers & Internet | Subjects | Books

General | Computers & Internet | Subjects | Books

General | Science | Subjects | Books

ASIN: 0387945598

Book Description

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning from the general point of view of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: - the general setting of learning problems and the general model of minimizing the risk functional from empirical data - a comprehensive analysis of the empirical risk minimization principle and shows how this allows for the construction of necessary and sufficient conditions for consistency - non-asymptotic bounds for the risk achieved using the empirical risk minimization principle - principles for controlling the generalization ability of learning machines using small sample sizes - introducing a new type of universal learning machine that controls the generalization ability.

Customer Reviews:

New to Field of Learning Theory.......2006-04-11

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.

Simple examples would significantly aid the readability and understandability of the text - akin to the way we teach our children. We don't describe all the attributes of a rabbit, we point to a picture of a rabbit and say "bunny". After two or three examples of this my children know the abstract concept of a rabbit (without me having to describe a small, four legged creature with long ears, etc. and then answering the inevitable question of "What's four legged creature mean daddy?"). Particularly with a text about learning theory, one would think it would be full of such examples - at least from a pedagogical point of view.

Initially, I didn't mind Vapnick's editorializing, but after a while I find it annoying - I'm sure he didn't single-handedly invent the entire field of statistical learning theory, but he sure doesn't miss any opportunities to tell the reader that he believes he has.

worth reading.......2001-09-22

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.
In a nutshell, what Vapnick shows, he shows very well, and is able to provide the "why" of things as no one else. What he doesn't show... you'll have to find somewhere else (the recent Book of Friedman Hastie & Tibs is an excellent starting point).
A last remark. The book is rich in grammatical errors and typos. They could have been corrected in the second edition, but do not detract from the book's readability.

A very nice book to get ideas on support vector machines.......2000-08-28

This is a very readable book by an authority on this subject. The book starts with the statistical learning theory, pioneered by the author and co-worker's work, and gradually leads to the path of discovery of support vector machines. An excellent and distinctive property of support vector machines is that they are robust to small data perturbation and have good generalization ability with function complexity being controlled by VC dimension. The treatment of nonlinear kernel classification and regression is given for the first time in the first edition. The 2nd edition includes significant updates including a separate chapter on support vector regression as well as a section on logistic regression using the support vector approach. Most computations involved in this book can be implemented using a quadratic programming package. The connections of support vector machines to traditional statistical modeling such as kernel density and regression and model selection are also discussed. Thus, this book will be an excellent starting point for learning support vector machines.

A research field described by the man who invented it.......2000-02-25

Vapnik and collaborators have developed the field of statistical learning theory underlying recent advances in machine learning and artificial intelligence (e.g. support vector machines). This book almost accomplishes the formidable task of comprehensibly describing the essential ideas of learning theory to non-statisticians. It contains ample theorems but almost no proofs.

Advances in Bayesian Networks (Studies in Fuzziness and Soft Computing)

Average customer rating:

Advances in Bayesian Networks (Studies in Fuzziness and Soft Computing)

Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

General | Certification Central | Computers & Internet | Subjects | Books

General | Computers & Internet | Subjects | Books

General | Engineering | Professional & Technical | Subjects | Books

General | Mathematics | Science | Subjects | Books

General | Arts & Photography | Subjects | Books

Look Inside Computer Books | Trip | Specialty Stores | Books
ASIN: 3540208763

Book Description

In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.

Bayesian Learning for Neural Networks (Lecture Notes in Statistics)

Average customer rating:

Excellent book on neural networks and Bayesian methods

Bayesian Learning for Neural Networks (Lecture Notes in Statistics)
Radford M. Neal
Manufacturer: Springer
ProductGroup: Book
Binding: Paperback

General | Certification Central | Computers & Internet | Subjects | Books

General | Computers & Internet | Subjects | Books

General | Science | Subjects | Books

All Titles | Qualifying Textbooks - Fall 2007 | Stores | Books

Computers & Internet | Qualifying Textbooks - Fall 2007 | Stores | Books

Professional | Qualifying Textbooks - Fall 2007 | Stores | Books

Science | Qualifying Textbooks - Fall 2007 | Stores | Books
Similar Items:

ASIN: 0387947248

Book Description

Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.

Customer Reviews:

Excellent book on neural networks and Bayesian methods.......1997-08-24

This book is a landmark in both neural networks and in statistics. It describes a coherent and powerful framework for using supervised neural networks, and it contains radical ways of making Bayesian Monte Carlo computations in high-dimensional spaces more efficient.

Advances in Intelligent Data Analysis: 4th International Conference, IDA 2001, Cascais, Portugal, September 13-15, 2001. Proceedings (Lecture Notes in Computer Science)

Average customer rating:

Advances in Intelligent Data Analysis: 4th International Conference, IDA 2001, Cascais, Portugal, September 13-15, 2001. Proceedings (Lecture Notes in Computer Science)

Manufacturer: Springer
ProductGroup: Book
Binding: Paperback

Data Mining | Databases | Computers & Internet | Subjects | Books

General | Databases | Computers & Internet | Subjects | Books

General | Software | Computers & Internet | Subjects | Books

General | Science | Subjects | Books

General | Medicine | Subjects | Books

All Amazon Upgrade | Amazon Upgrade | Stores | Books

Computers & Internet | Amazon Upgrade | Stores | Books

Medicine | Amazon Upgrade | Stores | Books

Professional & Technical | Amazon Upgrade | Stores | Books

Science | Amazon Upgrade | Stores | Books

All Titles | Qualifying Textbooks - Fall 2007 | Stores | Books

Computers & Internet | Qualifying Textbooks - Fall 2007 | Stores | Books

Medicine | Qualifying Textbooks - Fall 2007 | Stores | Books

Professional | Qualifying Textbooks - Fall 2007 | Stores | Books

Science | Qualifying Textbooks - Fall 2007 | Stores | Books
ASIN: 3540425810

Book Description

This book constitutes the refereed proceedings of the 4th International Conference on Intelligent Data Analysis, IDA 2001, held in Cascais, Portugal, in September 2001.The 37 revised full papers presented were carefully reviewed and selected from a total of almost 150 submissions. All current aspects of this interdisciplinary field are addressed; the areas covered include statistics, artificial intelligence, neural networks, machine learning, data mining, and interactive dynamic data visualization.

Advances in Probabilistic Graphical Models (Studies in Fuzziness and Soft Computing)

Average customer rating:

Advances in Probabilistic Graphical Models (Studies in Fuzziness and Soft Computing)

Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

General | Graphic Design | Computers & Internet | Subjects | Books

General | Engineering | Professional & Technical | Subjects | Books

General | Science | Subjects | Books

General | Mathematics | Science | Subjects | Books
ASIN: 354068994X

Book Description

In recent years considerable progress has been made in the area of probabilistic graphical models, in particular Bayesian networks and influence diagrams. Probabilistic graphical models have become mainstream in the area of uncertainty in artificial intelligence;
contributions to the area are coming from computer science, mathematics, statistics and engineering.

This carefully edited book brings together in one volume some of the most important topics of current research in probabilistic graphical modelling, learning from data and probabilistic inference. This includes topics such as the characterisation of conditional
independence, the sensitivity of the underlying probability distribution of a Bayesian network to variation in its parameters, the learning of graphical models with latent variables and extensions to the influence diagram formalism. In addition, attention is given to important application fields of probabilistic graphical models, such as the control of vehicles, bioinformatics and medicine.

Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems (Machine Learning Series)

Average customer rating:

Beginning to Age, But Great for Fundamentals
Still a good intro to predictive modeling

Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems (Machine Learning Series)
Sholom M. Weiss , and Casimir A. Kulikowski
Manufacturer: Morgan Kaufmann Publishers
ProductGroup: Book
Binding: Hardcover

General | Computers & Internet | Subjects | Books

General | Operating Systems | Computers & Internet | Subjects | Books

General | Science | Subjects | Books
Similar Items:

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

ASIN: 1558600655

Book Description

This book is a practical guide to classification learning systems and their applications. These computer programs learn from sample data and make predictions for new cases, sometimes exceeding the performance of humans.

Practical learning systems from statistical pattern recognition, neural networks, and machine learning are presented. The authors examine prominent methods from each area, using an engineering approach and taking the practitioner's viewpoint. Intuitive explanations with a minimum of mathematics make the material accessible to anyone--regardless of experience or special interests.

The underlying concepts of the learning methods are discussed with fully worked-out examples: their strengths and weaknesses, and the estimation of their future performance on specific applications. Throughout, the authors offer their own recommendations for selecting and applying learning methods such as linear discriminants, back-propagation neural networks, or decision trees. Learning systems are then contrasted with their rule-based counterparts from expert systems.

Customer Reviews:

Beginning to Age, But Great for Fundamentals.......2005-10-29

This is a classic for anyone interested in machine learning, data mining or predictive statistics. Though it is beginning to age, it covers essential aspects of empirical modeling still not covered by many more recent titles (!). A subsequent effort by one of the authors, "Predictive Data Mining" is a bit more current though shorter on the fundamentals.

Still a good intro to predictive modeling.......2000-04-14

This book gives a good cohesive introduction to the basic algorithms from Statistics, Machine Learning and Pattern Recognition research. These include Nearest Neighbor, Decision Trees, Bayesian Networks, and Neural Networks.

The main value of the book however is its coverage of techniques that 1) estimate a model's accuracy, and 2) select a 'good' model. This book offers the reader a solid foundation to what we are trying to achieve: to get at the objective truth.

Books:

Books Index

Books Home

Recommended Books