Information Theory, Inference & Learning Algorithms

Book Description

Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

Customer Reviews:

Outstanding book, especially for statisticians.......2007-10-02

I find it interesting that most of the people reviewing this book seem to be reviewing it as they would any other information theory textbook. Such a review, whether positive or critical, could not hope to give a complete picture of what this text actually is. There are many books on information theory, but what makes this book unique (and in my opinion what makes it so outstanding) is the way it integrates information theory with statistical inference. The book covers topics including coding theory, Bayesian inference, and neural networks, but it treats them all as different pieces of a unified puzzle, focusing more on the connections between these areas, and the philosophical implications of these connections, and less on delving into depth in one area or another.

This is a learning text, clearly meant to be read and understood. The presentation of topics is greatly expanded and includes much discussion, and although the book is dense, it is rarely concise. The exercises are absolutely essential to understanding the text. Although the author has made some effort to make certain chapters or topics independent, I think that this is one book for which it is best to more or less work straight through. For this reason and others, this book does not make a very good reference: occasionally nonstandard notation or terminology is used.

The biggest strength of this text, in my opinion, is on a philosophical level. It is my opinion, and in my opinion it is a great shame, that the vast majority of statistical theory and practice is highly arbitrary. This book will provide some tools to (at least in some cases) anchor your thinking to something less arbitrary. It's ironic that much of this is done within the Bayesian paradigm, something often viewed (and criticized) as being more arbitrary, not less so. But MacKay's way of thinking is highly compelling. This is a book that will not just teach you subjects and techniques, but will shape the way you think. It is one of the rare books that is able to teach how, why, and when certain techniques are applicable. It prepares one to "think outside the box".

I would recommend this book to anyone studying any of the topics covered by this book, including information theory, coding theory, statistical inference, or neural networks. This book is especially indispensable to a statistician, as there is no other book that I have found that covers information theory with an eye towards its application in statistical inference so well. This book is outstanding for self-study; it would also make a good textbook for a course, provided the course followed the development of the textbook very closely.

Great wish it had more n option inverse problems.......2007-07-16

This is fantastic book. Really takes an intuitive approach to the material. The explanation of occam's razor is worth the price of the whole book. Highly recommended.

Great Book As Far As It Goes.......2006-03-27

I have used this to get a good background in the topics covered, especially inference theory, and in general I found it to be great book which fills a market gap. The only sins I see are sins of omission. I personally would have enjoyed seeing a more task driven organization. I seem to need these methods periodically but I never seem to need the same method twice. Also, many of the techniques are heavily iterative, i.e., monte carlo, neural networks, etc. This is fine but much of what I do is in the context of simulations where 100,000 step iterative methods don't work so well because of resource constraints. Historically, that has been the problem with many of these methods. They are useful for relatively small domains but don't necessarily work that well for "real" problems. That is probably why more task oriented books are not available. Of course the author is following the outline of the current research into the subject manner which in turn is largely determined by "interesting" and "doable" problems. The real progess in this field will come when the problems are formulated more by what is needed in the nontraditional domains of application. A good example of a useful compression (and identification in some cases) technique that is not covered is Principal Component Analysis. Technically, it is in none of the technique domains covered in this book, but it would have been nice to see some of the methods in the book compared with PCA. The author does make the statement at one point that image recognition is an interesting problem for which the method being discussed at the time is used. Nevertheless, this is a great overview of the subject manner and is very entertaining. That in the long run probably explains the problem: it is a textbook.

A must have..........2005-03-01

Uniting information theory and inference in an interactive and entertaining way, this book has been a constant source of inspiration, intuition and insight for me. It is packed full of stuff - its contents appear to grow the more I look - but the layering of the material means the abundance of topics does not confuse.

This is _not_ just a book for the experts. However, you will need to think and interact when reading it. That is, after all, how you learn, and the book helps and guides you in this with many puzzles and problems.

Good value text on a spread of interesting and useful topics.......2005-02-20

I am a PhD student in computer science. Over the last year and a half this book has been invaluable (and parts of it a fun diversion).

For a course I help teach, the intoductions to probability theory and information theory save a lot of work. They are accessible to students with a variety of backgrounds (they understand them and can read them online). They also lead directly into interesting problems.

While I am not directly studying data compression or error correcting codes, I found these sections compelling. Incredibly clear exposition; exciting challenges. How can we ever be certain of our data after bouncing it across the world and storing it on error-prone media (things I do every day)? How can we do it without >60 hard-disks sitting in our computer? The mathematics uses very clear notation --- functions are sketched when introduced, theorems are presented alongside pictures and explanations of what's really going on.

I should note that a small number (roughly 4 or 5 out of 50) of the chapters on advanced topics are much more terse than the majority of the book. They might not be of interest to all readers, but if they are, they are probably more friendly than finding a journal paper on the same topic.

Most importantly for me, the book is a valuable reference for Bayesian methods, on which MacKay is an authority. Sections IV and V brought me up to speed with several advanced topics I need for my research.

Algorithms & Architectures (Neural Network Systems Techniques and Applications)

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Algorithms & Architectures (Neural Network Systems Techniques and Applications)

Manufacturer: Academic Press
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Binding: Hardcover

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Implementation Techniques (Neural Network Systems Techniques and Applications)

ASIN: 012443861X

Book Description

This volume is the first diverse and comprehensive treatment of algorithms and architectures for the realization of neural network systems. It presents techniques and diverse methods in numerous areas of this broad subject. The book covers major neural network systems structures for achieving effective systems, and illustrates them with examples.
This volume includes Radial Basis Function networks, the Expand-and-Truncate Learning algorithm for the synthesis of Three-Layer Threshold Networks, weight initialization, fast and efficient variants of Hamming and Hopfield neural networks, discrete time synchronous multilevel neural systems with reduced VLSI demands, probabilistic design techniques, time-based techniques, techniques for reducing physical realization requirements, and applications to finite constraint problems.
A unique and comprehensive reference for a broad array of algorithms and architectures, this book will be of use to practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as in computer science and engineering.

Key Features
* Radial Basis Function networks
* The Expand-and-Truncate Learning algorithm for the synthesis of Three-Layer Threshold Networks
* Weight initialization
* Fast and efficient variants of Hamming and Hopfield neural networks
* Discrete time synchronous multilevel neural systems with reduced VLSI demands
* Probabilistic design techniques
* Time-based techniques
* Techniques for reducing physical realization requirements
* Applications to finite constraint problems
* Practical realization methods for Hebbian type associative memory systems
* Parallel self-organizing hierarchical neural network systems
* Dynamics of networks of biological neurons for utilization in computational neuroscience
Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as in computer science and engineering, will find this volume a unique and comprehensive reference to a broad array of algorithms and architectures

Customer Reviews:

What was I reading?.......1999-10-05

I thought I was intelligent but this book put me in my place. I was able to get through the book because it was written in english, but what difference does that make. The book gets three stars because being able to put words like that in sentences is an accomplishment unto itself.

Neural, Novel & Hybrid Algorithms for Time Series Prediction

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No idea for whom it was written
Excellent work
Not for beginners
Low then avarage book...
-

Neural, Novel & Hybrid Algorithms for Time Series Prediction
Timothy Masters
Manufacturer: Wiley
ProductGroup: Book
Binding: Paperback

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ASIN: 0471130419

Book Description

An authoritative guide to predicting the future using neural, novel, and hybrid algorithms

Expert Timothy Masters provides you with carefully paced, step-by-step advice and guidance plus the proven tools and techniques you need to develop successful applications for business forecasting, stock market prediction, engineering process control, economic cycle tracking, marketing analysis, and more. Neural, Novel & Hybrid Algorithms for Time Series Prediction provides information on:
* Robust confidence intervals for predictions made with neural, ARIMA, and other models
* Wavelets for detecting features that presage important events
* Multivariate ARMA models for simultaneous prediction of multiple series based on multiple inputs and shocks
* Hybrid ARMA/neural models to improve the accuracy of predictions
* Data reduction and orthogonalization using principal components and related operations
* Digital filters for preprocessing to enhance useful information and suppress noise
* Diagnostic tools such as the maximum entropy spectrum and Savitzky-Golay filters for suggesting and validating prediction models
* Effective preprocessing techniques for prediction with neural networks

CD-ROM INCLUDES:
* PREDICT-both DOS and Windows NT versions-a powerful time series program that can be easily customized to make accurate predictions in any application area
* Much useful source code, including the complex-general multivariate fast Fourier transform in both C++ and Pentium-optimized assembler

Customer Reviews:

No idea for whom it was written.......2007-09-19

Do not buy this book unless you already read it in a library and just like it to be on the shelf. All ideas are very well known and code is almost useless.
I found much better sources for free on the web.

Excellent work.......2003-07-16

I usually don't pay attention to readers's comments or reviews.
Because after all this is a very subjective matter. I bought the book for two reasons neither of which has to do with a reader's review. The first reason was because I have a copy of Timothy's book on Practical Neural Networks in C++, which I found excellent, and the second reason was because I had previewed chapter one before I bought the book and liked it very much for what it had to say and the way it said it. Timothy's books are for a wide audience of intelligent people, not necessarily all rocket scientists, and although a mathematician himself, restricts math as much as possible so people do not get bogged down by the math and loose the forest for the trees. On the other hand there is sufficient amount of bibliography for any one who is interested to pursue most rigorous or more exotic approaches. The code examples are good and the executable file NPREDICT, works without any further processing, for those who don't want to mess with code and compiling. The treatment of Box-Jekins ARMA model,and the multivariate example on temperature and precipitation is very good. The book is highly recomended to any one who has little or no knowledge of the subject, and wants to understand what time series is all about

Not for beginners.......2001-10-28

Prediction methods for time series are a multi-million dollar industry and are of upmost importance in financial engineering, weather prediction, logistics, network modeling, and myriads of other fields. This book gives an overview of various methodologies for time series prediction, and is written for readers with substantial experience in this area. The author emphasizes that time series prediction is more of an art rather than a science, with the practitioner usually employing hybrids of established techniques, only some of which have a rigorous mathematical foundation. In fact, despite the subject matter, this book is very lean on mathematics, and the reader will have to consult other books for a more detailed mathematical treatment. The NPredict package accompanying the book is designed to run on an NT and a DOS platform, and illustrates the main points in the book. Readers who have familiarity with the authors earlier books on neural networks will definitely find this one easier to follow. It is, again, not written for beginning students, but the author does a fairly good job of presenting the material for the advanced reader.

Low then avarage book..........2001-09-09

Probably my impression would be better if author setup right expectation for this book. As a mathematically inclined person I was disappointed by fact that author left all explanation of crucial concepts and algorithms behind the scenes just referencing "It is described in other books" - see subjects about Maximum Entropy Methods, details in ARIMA. Reader is left to take some concepts for granted without clear understanding of subject. Meantime author missed some important subjects in Neural Networks and even digital filters like recursive digital filters which proved to be superior to filters described in the book. The rest of theory is quite heuristic and based on unproved concepts and author's "feelings" that is not acceptable at least for me. Sometimes author refers to the program code to explain methods but the quality of code whish to be better. He states about code that "It is wildly extravagant in its memory usage in order to save a small amount of execution time. This reflects modern hardware characteristics". Unfortunately this concept is wrong because modern software design requires simple and clear code rather then weird code with questionable improvement in performance, which is difficult to use and read. Shortly, I would not recommend to buy or use this book because it might be only useful for beginning programmers who does not care about the subject to code.

-.......2000-04-18

Not much on neural nets. A good overview of a signals and systems textbook for those who want to learn about filters without all the math. I was disappointed that there wasn't a results section for the NPREDICT tool, just a bunch of flags and parameter garbage to tweak.

Fundamentals of Natural Computing: Basic Conepts, Algorithms, and Applications (Chapman & Hall/Crc Computer and Information Sciences)

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Great book

Fundamentals of Natural Computing: Basic Conepts, Algorithms, and Applications (Chapman & Hall/Crc Computer and Information Sciences)
Leandro N. de Castro
Manufacturer: CRC Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 1584886439

Book Description

Natural computing brings together nature and computing to develop new computational tools for problem solving; to synthesize natural patterns and behaviors in computers; and to potentially design novel types of computers. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications presents a wide-ranging survey of novel techniques and important applications of nature-based computing. This book presents theoretical and philosophical discussions, pseudocodes for algorithms, and computing paradigms that illustrate how computational techniques can be used to solve complex problems, simulate nature, explain natural phenomena, and possibly allow the development of new computing technologies. The author features a consistent and approachable, textbook-style format that includes lucid figures, tables, real-world examples, and different types of exercises that complement the concepts while encouraging readers to apply the computational tools in each chapter. Building progressively upon core concepts of nature-inspired techniques, the topics include evolutionary computing, neurocomputing, swarm intelligence, immunocomputing, fractal geometry, artificial life, quantum computing, and DNA computing. Fundamentals of Natural Computing is a self-contained introduction and a practical guide to nature-based computational approaches that will find numerous applications in a variety of growing fields including engineering, computer science, biological modeling, and bioinformatics.

Customer Reviews:

Great book.......2007-02-18

I have to study about data mining and the professor recommended this book. After reading it, I think it's a great one. No wonder why the professor likes it.

Machine Vision (Signal Processing and Its Applications Series)

Average customer rating:

Good survey of specific machine vision techniques
Solid Foundation to computer Vision
Excellent resource
Good structured reference, very useful

Machine Vision (Signal Processing and Its Applications Series)
E. R. Davies
Manufacturer: Academic Press
ProductGroup: Book
Binding: Paperback

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ASIN: 012206092X

Book Description

The field of machine vision has expanded extensively since the First Edition of Machine Vision was published by Academic Press in 1990. As a result, this Second Edition contains significant amounts of new material on artificial neural networks, mathematical morphology, motion, invariance, texture analysis, x-ray inspection, and foreign object detection. Intermediate level vision is examined in depth (especially Hough transforms), and automated visual inspectionis discussed. The author takes care to consider theoretical aspects as well as practical applications, including perspective invariants and robust statistics. Written in a user-friendly style and full of up-to-date methods, Machine Vision, Second Edition will be an essential volume for students and professionals in the field.

Key Features
* Gives considerable emphasis to robust analysis of images to demonstrate how problems of occlusion, noise, distortion, and variability may be overcome
* Introduces Hough transforms as an integral part of the text and shows how they may be applied in a variety of situations
* Presents the topic of robust statistics non-mathematically in the context of vision algorithms
* Considers the role of neural networks in machine vision
* Shows how the concepts of perspective invariance provide basic strategies for 2-D and 3-D vision
* Studies image transformations and the prespective n-point problem systematically to clarify how interpretation may proceed in various geometrical situations
* Pays special attention to the detection of defects, foreign objects, and real-time implementation hardware in consideration of automated visual inspection

Customer Reviews:

Good survey of specific machine vision techniques.......2006-06-17

To begin with, the latest edition of this book was published in 2004, so all reviews dated earlier than that are referring to a previous edition. This book is a good one on issues and algorithms as they pertain to machine vision versus general computer vision. If you want a good general textbook on computer vision try "Computer Vision" by Linda Shapiro. It has all of the background material and a firm foundation in all of the topics you would expect in a course on computer vision. This book also has a section on introductory computer vision topics, I just don't think it is as clear and as comprehensive as Shapiro's book, especially for students.

However, if you want an excellent treatment of the kinds of problems specific to machine vision - the detection of lines, holes, corners, circles, elipses, and polygons, for example, along with specific algorithm details, this book is very good. It also has good sections on pattern matching, motion estimation, and 3D machine vision. I would recommend it especially for those individuals who are already familiar with the basics of computer vision and would like a book on algorithms for solving specific problems in machine vision. I notice that Amazon only shows the table of contents for the previous edition, so I show the table of contents for the new edition next:

1. Vision, The Challenge

PART 1 - LOW-LEVEL VISION
2. Images and Imaging Operations
3. Basic Image Filtering Operations
4. Thresholding Techniques
5. Edge Detection
6. Binary Shape Analysis
7. Boundary Pattern Analysis
8. Mathematical Morphology

PART 2 - INTERMEDIATE-LEVEL VISION
9. Line Detection
10. Circle Detection
11. The Hough Transform and Its Nature
12. Ellipse Detection
13. Hole Detection
14. Polygon and Corner Detection
15. Abstract Pattern Matching Techniques

PART 3 - 3D VISION AND MOTION
16. The Three-Dimensional World
17. Tackling the Perspective n-Point Problem
18. Motion
19. Invariants and their Applications
20. Egomotion and Related Tasks
21. Image Transformations and Camera Calibration

Part 4 - TOWARDS REAL-TIME PATTERN RECOGNITION SYSTEMS
22. Automated Visual Inspection
23. Inspection of Cereal Grains
24. Statistical Pattern Recognition
25. Biologically Inspired Recognition Schemes
26. Texture
27. Image Acquisition
28. Real-Time Hardware and Systems Design Considerations

PART 5 - PERSPECTIVES ON VISION
29. Machine Vision, Art or Science?

Solid Foundation to computer Vision.......2002-02-20

First of all I like this book very much. This book provides a solid and concrete foundation to computer vision from engineering point of view. The basic issues are treated very well in the conceptual and practical levels (e.g. edge detection). I came from a photogrammetry background, which means that the geometric aspects are very dominant in my thinking, and this book emphasize many geometric concepts in computer vision specially the treatment of Hough Transform as a main theme in the book. I recommend this book to the practitioners in spatial sciences (GIS, Remote sensing, Photogrammetry, etc) as well as the general community of computer vision.

Excellent resource.......2001-08-04

Covers many aspects of vision, from basic image processing through high level scene analysis. It doesn't always go down to the nitty-gritty source code level for every topic, but it does provide the direction to handle most every common machine vision problem. Of the ten or so general machine vision books on my easy-access shelf, this is the one I seem to pull down the most.

Good structured reference, very useful.......2000-06-06

A very clearly structured book which is useful as a reference. Covers a lot of subjects (filtering, detection of shapes [lines, circles, holes and more], pattern matching/recognition, motion, invariants, ...), including the implementation aspects (hard/software). The chapters sometimes do not go much into deep but provide further references. Recommended book!

Applications of Soft Computing: Recent Trends (Advances in Soft Computing)

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Applications of Soft Computing: Recent Trends (Advances in Soft Computing)

Manufacturer: Springer
ProductGroup: Book
Binding: Paperback

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ASIN: 3540291237

Book Description

Soft Computing is a complex of methodologies that embraces approximate reasoning, imprecision, uncertainty and partial truth in order to mimic the remarkable human capability of making decisions in real-life, ambiguous environments. Soft Computing has therefore become popular in developing systems that encapsulate human expertise. Applications of Soft Computing: Recent Trends contains a collection of papers that were presented at the 10th Online World Conference on Soft Computing in Industrial Applications, held in September 2005. This carefully edited book provides a comprehensive overview of the recent advances in the industrial applications of soft computing and covers a wide range of application areas, including optimisation, data analysis and data mining, computer graphics and vision, prediction and diagnosis, design, intelligent control, and traffic and transportation systems. The book is aimed at researchers and professional engineers who are engaged in developing and applying intelligent systems. It is also suitable as wider reading for science and engineering postgraduate students.

Learning and Generalization: With Applications to Neural Networks

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Learning and Generalization: With Applications to Neural Networks
Mathukumalli Vidyasagar
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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Pattern Recognition and Machine Learning (Information Science and Statistics)

ASIN: 1852333731

Book Description

Learning and Generalization provides a formal mathematical theory for addressing intuitive questions such as:

â€¢ How does a machine learn a new concept on the basis of examples?

â€¢ How can a neural network, after sufficient training, correctly predict the outcome of a previously unseen input?

â€¢ How much training is required to achieve a specified level of accuracy in the prediction?

â€¢ How can one identify the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite interval of time?

In its successful first edition, A Theory of Learning and Generalization was the first book to treat the problem of machine learning in conjunction with the theory of empirical processes, the latter being a well-established branch of probability theory. The treatment of both topics side-by-side leads to new insights, as well as to new results in both topics.

This second edition extends and improves upon this material, covering new areas including:

â€¢ Support vector machines.

â€¢ Fat-shattering dimensions and applications to neural network learning.

â€¢ Learning with dependent samples generated by a beta-mixing process.

â€¢ Connections between system identification and learning theory.

â€¢ Probabilistic solution of 'intractable problems' in robust control and matrix theory using randomized algorithm.

Reflecting advancements in the field, solutions to some of the open problems posed in the first edition are presented, while new open problems have been added.

Learning and Generalization (second edition) is essential reading for control and system theorists, neural network researchers, theoretical computer scientists and probabilist.

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Evolutionary Computer Music

Manufacturer: Springer
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Binding: Paperback

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ASIN: 1846285992

Book Description

The evolutionary computation approach to music is an exciting new development for composers and musicologists alike. For composers, it provides an innovative and natural means for generating musical ideas from a specifiable set of primitive components and processes. For musicologists, these techniques are used to model the cultural transmission and change of a population's body of musical ideas over time. In both cases, musical evolution can be guided by a variety of constraints and tendencies built into the system, such as realistic psychological factors that influence the way music is expressed, experienced, learned, stored, modified, and passed on among individuals.

This book discusses not only the applications of evolutionary computation to music, but also the tools needed to create and study such systems. These tools are drawn in part from research into the origins and evolution of biological organisms, ecologies, and cultural systems on the one hand, and from computer simulation methodologies on the other. They can be combined to create surrogate artificial worlds populated by interacting simulated organisms in which complex musical experiments can be performed that would otherwise be impossible.

This authoritative book, with contributions from experts from around the globe, demonstrates that evolutionary systems can be used to create and to study musical compositions and cultures in ways that have never before been achieved.

Eduardo Reck Miranda is a Professor in Computer Music at the University of Plymouth, UK, where he heads the Interdisciplinary Centre for Computer Music Research (ICCMR). He has recently been appointed the Edgard VarÃ¨se Guest Professor of Computer Music at the Technical University of Berlin.

Al Biles is a Professor and the Undergraduate Program Coordinator in the Department of Information Technology at the Rochester Institute of Technology in Rochester, New York. Between performances with GenJam over the last thirteen years, he has been active in helping establish information technology as a recognized academic discipline.

Emergent Computing Methods in Engineering Design: Applications of Genetic Algorithms and Neural Networks (NATO ASI Series / Computer and Systems Sciences)

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Emergent Computing Methods in Engineering Design: Applications of Genetic Algorithms and Neural Networks (NATO ASI Series / Computer and Systems Sciences)

Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 3540608737

Book Description

This volume presents a collection of papers addressing aspects of the application of emergent computing paradigms in engineering design. Papers on genetic algorithms and evolutionary computing discuss proposed improvements to computing methodology as well as applications in engineering design. Papers on neural networks study theoretical issues of interpretation as well as their potential use as approximation tools in design and their applications in embedded and fuzzy control systems. Other papers deal with such topics as the combined use of genetic algorithms and neural networks, applications of the simulated annealing approach, problem decomposition techniques, and computer recognition and interpretation of objects. The book shows the tremendous potential of emergent methods in engineering design.

Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods (Genetic and Evolutionary Computation)

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Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods (Genetic and Evolutionary Computation)
Nikolay Nikolaev , and Hitoshi Iba
Manufacturer: Springer
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ASIN: 0387312390

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This book delivers theoretical and practical knowledge for developing algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The text emphasizes an organized model identification process by which to discover models that generalize and predict well. The empirical investigations detailed here demonstrate that PNN models evolved by genetic programming and improved by backpropagation are successful when solving real-world tasks.

Adaptive Learning of Polynomial Networks is a vital reference for researchers and practitioners in the fields of evolutionary computation, artificial neural networks and Bayesian inference, and for advanced-level students of genetic programming. Readers will strengthen their skills in creating efficient model representations and learning operators that efficiently sample the search space, and in navigating the search process through the design of objective fitness functions.

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