Introduction To Statistical Pattern Recognition (Computer Science and Scientific Computing Series)

Book Description

This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises.

Customer Reviews:

A best book on Statistical Pattern Recognition.......2005-09-13

Multivariate analysis is borrowed to name a NEW subject, Statistical Pattern Recognition (SPR). Many statisticians think it unfair or a shame. In spite of these, it is a good reference book of SPR. :-)

[1] Many contents of this book can be found in any graduate textbook of Multivariate Analysis, for instance, Fisher's linear disciminant, etc.
[2] The book is badly printed. Why not using LaTeX?
[3] Guassian distribution is assumed here and there.
[4] It may be good as a reference book, but definitely not as a textbook.

Standard reference and a classic text but with flaws.......2004-01-20

I do not like to consult this book for the following, quite superficial reason. The book is sloppily produced and proofread
(and the fault is [probably] mainly the publisher's instead of the author's). This manifests itself, e.g., as follows

(1) the typography is flawed (the equations hurt at least my eyes);
(2) at its each appearance, the all-important > < -sign goes the wrong way.

good coverage for engineers.......2000-08-04

Fukunaga is a standard source for pattern recognition methods often cited in the engineering literature. Covers parametric (particularly linear and quadratic discriminant algorithms) and nonparametric methods (density estimation). It is designed for and popular with engineers. When I was working at Nichols Research Corporation Fukunaga's papers and this book (earlier edition) were often cited as sources to justify the algorithms we used for discrimination problems. In fact Fukunaga had been a consultant to the company (used primarily by the Boston branch of the company where the KENN algorithms were developed). It is a reputable source. I still like Duda and Hart (1972) for good explanations of the fundamental concepts. For statisticians McLachlan's book is now far and away the best source.

Standard Reference in the Field.......2000-04-06

If you are writing a machine learning paper, and need to cite something to support an argument, you can almost always cite Fukunaga. This work is a standard reference in the field. The presentation of most material is very terse, but that is great if you already have a good feel for the material and need to look up some details about some algorithm or technique. There isn't much about neural networks here, but for the rest of the pattern recognition techniques, this is almost always the first place to start. Another strong point for this book is the use of realistic examples, which illustrate many of the statistical techniques.

Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)

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It is my bible!

Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)
Gilles Aubert , and Pierre Kornprobst
Manufacturer: Springer
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Binding: Hardcover

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ASIN: 0387322000

Book Description

Partial differential equations (PDEs) and variational methods were introduced into image processing about fifteen years ago. Since then, intensive research has been carried out. The goals of this book are to present a variety of image analysis applications, the precise mathematics involved and how to discretize them.

Thus, this book is intended for two audiences. The first is the mathematical community by showing the contribution of mathematics to this domain. It is also the occasion to highlight some unsolved theoretical questions. The second is the computer vision community by presenting a clear, self-contained and global overview of the mathematics involved in image processing problems. This work will serve as a useful source of reference and inspiration for fellow researchers in Applied Mathematics and Computer Vision, as well as being a basis for advanced courses within these fields.

During the four years since the publication of the first edition, there has been substantial progress in the range of image processing applications covered by the PDE framework. The main goals of the second edition are to update the first edition by giving a coherent account of some of the recent challenging applications, and to update the existing material. In addition, this book provides the reader with the opportunity to make his own simulations with a minimal effort. To this end, programming tools are made available, which will allow the reader to implement and test easily some classical approaches.

Customer Reviews:

It is my bible!.......2004-07-01

This book not only includes the state-of-art image processig techniques using math methods, but also provides very good numerical schemes for these methods. This book is written by mathematicians, but engineers still can easily implemente many commonly used image processing algorithm, just follow the detailed numerical difference schemes provided in the appendix.

Mathematical Methods in Artificial Intelligence (Practitioners)

Average customer rating:

Good, but somewhat outdated
Interesting but content bit disconnected
Excellent
It is a useful book for research oriented readers.

Mathematical Methods in Artificial Intelligence (Practitioners)
Edward A. Bender
Manufacturer: Wiley-IEEE Computer Society Pr
ProductGroup: Book
Binding: Paperback

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ASIN: 0818672005

Book Description

Mathematical Methods in Artificial Intelligence introduces the student to the important mathematical foundations and tools in AI and describes their applications to the design of AI algorithms. This useful text presents an introductory AI course based on the most important mathematics and its applications. It focuses on important topics that are proven useful in AI and involve the most broadly applicable mathematics.

The book explores AI from three different viewpoints: goals, methods or tools, and achievements and failures. Its goals of reasoning, planning, learning, or language understanding and use are centered around the expert system idea. The tools of AI are presented in terms of what can be incorporated in the data structures. The book looks into the concepts and tools of limited structure, mathematical logic, logic-like representation, numerical information, and nonsymbolic structures.

The text emphasizes the main mathematical tools for representing and manipulating knowledge symbolically. These are various forms of logic for qualitative knowledge, and probability and related concepts for quantitative knowledge. The main tools for manipulating knowledge nonsymbolically, as neural nets, are optimization methods and statistics. This material is covered in the text by topics such as trees and search, classical mathematical logic, and uncertainty and reasoning. A solutions diskette is available, please call for more information.

Customer Reviews:

Good, but somewhat outdated.......2007-06-21

This is a good introductory text in the mathematical backgound of AI. It covers the problems of searches, logic programming, different types of reasoning, neural networks as well as a little bit of probabilities.

Its great merit consists in the fact that it is not disconnected from the realities of the world. The chapters in Prolog, for instance, are well developed and the mathematical foundation of this programming language is quite thoroughly explained. This is rare to find in Prolog or logic books; most of them are either too pragmatic or too theoretical. This book makes a nice balance between the two.

The book has some drawbacks, though. First and foremost, it is geared a little bit to much on logic at the expense of other intelligent forms of computing (pattern recognition - be it vision, speech or handwriting, planning, constraints processing, theorem proving, case-based reasoning, to name just a few).

For example, the section dedicated to stochastic processing is ridiculously small.

However, as a good introduction into the math of AI, this book lives well up to expectations.

Interesting but content bit disconnected.......2007-06-08

Most topics are interesting and contribute to an understanding of AI. Only point of confusion is some sections seem more like authors personal issue rather than a connected discussion of AI. Expected more because of the many recommendations for the authors work.

Excellent.......2002-07-27

Although using only elementary mathematics, and not at all addressing new areas of artificial intelligence, such as inductive logic programming, this book gives an excellent overview of how mathematics is used in artificial intelligence. Mathematics at all levels is used in this field, both in the algorithms and in discussing its foundations, and this book serves as a good introduction to its application in A.I. Only elementary algebra and calculus are used in the book, making it very accessible to the beginning student in computer science. Readers with more sophisticated background in mathematics can then extend the results in the book to more advanced mathematical contexts. The author's writing style is very informal, and in many places in the book he encourages the reader to "stop and think" before continuing in the reading. Exercises, some simple and some very challenging, are found at the end of most chapter sections.

The author gives a brief overview of the history of A.I. in chapter one, including a discussion of the issues of computational complexity in A.I. algorithms, a discussion of expert systems (with examples), and a few biographical sketches.

Chapter 2 is a fairly detailed overview of search algorithms, and the author introduces some notions from the mathematical field of combinatorics, namely directed graphs and ordered trees. Induction and recursion are then reviewed as tools for search algorithms. The recursive formulation of algorithms in A.I. is of course very powerful, and one that students need to master early on. Fields such as bioinformatics and data mining are becoming increasingly dependent on search algorithms from A.I., and the author reviews these in detail, including 'simple' search methods such as breadth-first, depth-first, and iterative-deepening, along with 'heuristic' methods.

The reader gets introduced to first-order predicate calculus in chapter 3. This topic could be said to be one of the most important ones in A.I., and it is discussed in this chapter using the (declarative) programming language Prolog. One could easily use the language Lisp, but Prolog makes more apparent the head/body clause structure of predicate logic. In addition, if a reader wants to move on to more modern developments in A.I., such as inductive logic programming, which can be viewed essentially as predicate logic but with inductive reasoning, a mastery of the content of this chapter is essential.

Chapter 4 introduces the reader to the proof theory, namely the technique of resolution, which is discussed for propositional calculus, where it is very simple, and for predicate logic, in the latter wherein some specialized techniques must be brought in, such as Skolemization. The author also discussed proof in the context of Prolog, and introduces the cut operator, which inhibits Prolog from fully implementing resolution. He also gives an interesting discussion on the problem of negation in Prolog and the closed-world assumption.

The author has been careful to not write a purely theoretical book in computer science, and evidence of this is given in chapter 5, which discusses how to implement first-order logic (FOL) into real-world applications. It is one thing to discuss the properties of logic, quite another to actually use it productively to solve problems of interest. The author discusses the limitations of FOL in these applications, and how they can be resolved through alternative reasoning tools, such as nonmonotonic logics, Bayesian networks, and fuzzy sets.

One of these alternatives, nonmonotonic reasoning, is discussed in the next chapter, wherein the author gives a fairly detailed overview of default reasoning and how it is implemented in Prolog. Rule sets and semantic nets are also discussed, along with defeasible reasoning. Applications of these techniques are stymied by their computational complexity, and the author gives references for discussions of this.

After a review of probability theory in chapter 7, the author discusses Bayesian networks in chapter 8. These have been extremely important in recent applications of A.I., and the author gives a fine review of their properties, especially their ability to incorporate causality by imposing a directed graph structure on the event space. The author gives a few examples of Bayesian networks, including a medical diagnosis, wherein he introduces a very important concept in A.I., namely that of abductive inference. Detailed discussion (with proofs) is given for the Kim-Pearl algorithm for singly connected networks.

Chapter 9 is an introduction to fuzzy logic and belief theory. The author motivates nicely the reasons for considering fuzzy reasoning instead of probabilistic methods. The Dempster-Shafer belief theory, which has become popular in recent years, is also discussed in some detail.

So as to motivate the discussion of neural networks, the next chapter overviews automatic pattern classification. Contrasting between supervised and unsupervised learning of patterns, the author then outlines the types of automatic classifiers, such as decision trees and neural networks. The chapter on neural networks is a good introduction considering the vastness of the subject. Indeed, an enormous amount of research has been done on neural networks, and their use in applications of A.I. has finally been achieving success in recent years.

Concepts from information theory are of course very important in A.I. and these are discussed in chapter 12, along with more advanced topics in probability and statistics that were not treated earlier in the book. These ideas are used in the next chapter wherein neural networks and decisions trees are discussed in more detail. The most interesting part of this discussion is the idea that noise can improve the generalization capabilities of neural networks. This strategy will be obvious to the physicist reader who has studied the effects of noise on dynamical systems governed by potentials with local minima.

The last chapter of the book discusses some additional topics that should be included in a study of A.I., such as genetic algorithms and more discussion of optimization, such as simulated annealing. Hidden Markov models are also briefly discussed, and this is somewhat disappointing given their importance in current applications. The reader is also introduced to robotics, certainly the most exciting of all topics in 21st century A.I.

It is a useful book for research oriented readers........2000-03-27

Most AI books do not emphasize the mathematical issues. Consequently, the readers face difficulty to read journals. This is a highly recommended book for those research oriented readers. It requires no formal background of mathematics beyond high school level. I read the book several times. It helped me a lot to understand many difficult papers. Among the chapters the most useful are chapter 6 on nonmonotonic reasoning and chapter 8 on Bayesian networks. The beginners will find chapter 3 and 4 on predicate logic and the theory of resolution highly useful. I strongly feel that the book should be read by all people working in the domain of AI.

Elements of Finite Model Theory (Texts in Theoretical Computer Science. An EATCS Series)

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Technical review of this book

Elements of Finite Model Theory (Texts in Theoretical Computer Science. An EATCS Series)
L. Libkin
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 3540212027

Book Description

This book is an introduction to finite model theory which stresses the computer science origins of the area. In addition to presenting the main techniques for analyzing logics over finite models, the book deals extensively with applications in databases, complexity theory, and formal languages, as well as other branches of computer science. It covers Ehrenfeucht-FraÃ¯ssÃ© games, locality-based techniques, complexity analysis of logics, including the basics of descriptive complexity, second-order logic and its fragments, connections with finite automata, fixed point logics, finite variable logics, zero-one laws, and embedded finite models, and gives a brief tour of recently discovered applications of finite model theory.

This book can be used both as an introduction to the subject, suitable for a one- or two-semester graduate course, or as reference for researchers who apply techniques from logic in computer science.

Customer Reviews:

Technical review of this book.......2004-09-21

Model theory is the study of the logical properties of mathematical structures. Finite model theory arises when we focus our attention on finite structures, such as finite graphs (graphs with a finite number of nodes). This book presents the most important results of finite model theory in an extremely readable, yet careful and precise manner. Libkin himself is a master of the art, and this shows in his beautiful presentation of the material.
--Ronald Fagin Manager, Foundations of Computer Science, IBM Almaden Research Center, San Jose, CA

Average customer rating:

goes beyond the idea of a probability distribution

Graphical Belief Modeling
Russell G. Almond
Manufacturer: Chapman & Hall/CRC
ProductGroup: Book
Binding: Loose Leaf

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ASIN: 0412066610

Book Description

This innovative volume explores graphical models using belief functions as a representation of uncertainty, offering an alternative approach to problems where probability proves inadequate. Graphical Belief Modeling makes it easy to compare the two approaches while evaluating their relative strengths and limitations. The author examines both theory and computation, incorporating practical notes from the author's own experience with the BELIEF software package. As one of the first volumes to apply the Dempster-Shafer belief functions to a practical model, a substantial portion of the book is devoted to a single example--calculating the reliability of a complex system. This special feature enables readers to gain a thorough understanding of the application of this methodology. The first section provides a description of graphical belief models and probablistic graphical models that form an important subset: the second section discusses the algorithm used in the manipulation of graphical models: the final segment of the book offers a complete description of the risk assessment example, as well as the methodology used to describe it. Graphical Belief Modeling offers researchers and graduate students in artificial intelligence and statistics more than just a new approach to an old reliability task: it provides them with an invaluable illustration of the process of graphical belief modeling.

Customer Reviews:

goes beyond the idea of a probability distribution .......2006-05-15

A belief function is a step beyond traditional probability distribution functions. The latter describe uncertainty. But, by definition, you somehow know precisely that a given pdf is a correct description of a process. A belief function tries to express the imprecision in knowledge about a pdf.

The book is an advanced treatment of how graphical methods can be used to aid in the construction of belief models. Be aware though that the book has complicated ideas drawn from set theory and graph theory. With rigourous derivations of theorems.

Usages are also stressed. Examples are given of how to model failure rates in systems of many parts. Where a traditional probabilistic analysis may be far too difficult or labourious.

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.

Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge (Artificial Intelligence)

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Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge (Artificial Intelligence)
Benjamin Kuipers
Manufacturer: The MIT Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 026211190X

Book Description

This book presents, within a conceptually unified theoretical framework, a body of methods that have been developed over the past fifteen years for building and simulating qualitative models of physical systems - bathtubs, tea kettles, automobiles, the physiology of the body, chemical processing plants, control systems, electrical systems - where knowledge of that system is incomplete. The primary tool for this work is the author's QSIM algorithm, which is discussed in detail.

Qualitative models are better able than traditional models to express states of incomplete knowledge about continuous mechanisms. Qualitative simulation guarantees to find all possible behaviors consistent with the knowledge in the model. This expressive power and coverage is important in problem solving for diagnosis, design, monitoring, explanation, and other applications of artificial intelligence.

The framework is built around the QSIM algorithm for qualitative simulation and the QSIM representation for qualitative differential equations, both of which are carefully grounded in continuous mathematics. Qualitative simulation draws on a wide range of mathematical methods to keep a complete set of predictions tractable, including the use of partial quantitative information. Compositional modeling and component-connection methods for building qualitative models are also discussed in detail.

Qualitative Reasoning is primarily intended for advanced students and researchers in AI or its applications. Scientists and engineers who have had a solid introduction to AI, however, will be able to use this book for self-instruction in qualitative modeling and simulation methods.

Artificial Intelligence series

Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling

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Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling
Clark Glymour , Richard Scheines , Peter Spirtes , and Kevin Kelly
Manufacturer: Academic Press
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Binding: Hardcover

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ASIN: 0122869613

Automated Deduction in Nonclassical Logics: Efficient Matrix Proof Methods for Modal and Intuitionistic Logics (Artificial Intelligence)

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Automated Deduction in Nonclassical Logics: Efficient Matrix Proof Methods for Modal and Intuitionistic Logics (Artificial Intelligence)
Lincoln A. Wallen
Manufacturer: The MIT Press
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Binding: Hardcover

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Book Description

This book develops and demonstrates efficient matrix proof methods for automated deduction within an important and comprehensive class of first order and intuitionistic logics. Traditional techniques for the design of efficient proof systems are abstracted from their original setting which allows their application to a wider class of mathematical logic. The logics discussed are used throughout computer science and artificial intelligence.

Contents: Introduction I. Automated Deduction in Classical Logic. Proof search in classical sequent calculi. A matrix characterization of classical validity. II. Automated Proof Deduction in Modal Logics. The semantics and proof theory of modal logics. Proof search in modal sequent calculi. Matrix characterizations of modal validity. Alternative proof methods for modal logics. Matrix based proof search. III. Automated Deduction in Intuitionistic Logic. A Matrix proof method. Conclusions.

Lincoln A. Wallen is a B.P. Venture Research Fellow at the University of Texas at Austin Automated Deduction in Nonclassical Logics is included in the Artificial Intelligence series, edited by Patrick Winston Michael Brady, and Daniel Bobrow.

Mechanizing Mathematical Reasoning: Essays in Honor of Jörg H. Siekmann on the Occasion of His 60th Birthday (Lecture Notes in Computer Science)

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Mechanizing Mathematical Reasoning: Essays in Honor of Jörg H. Siekmann on the Occasion of His 60th Birthday (Lecture Notes in Computer Science)

Manufacturer: Springer
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Binding: Paperback

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ASIN: 3540250514

Book Description

By presenting state-of-the-art results in logical reasoning and formal methods in the context of artificial intelligence and AI applications, this book commemorates the 60th birthday of JÃ¶rg H. Siekmann.

The 30 revised reviewed papers are written by former and current students and colleagues of JÃ¶rg Siekmann; also included is an appraisal of the scientific career of JÃ¶rg Siekmann entitled "A Portrait of a Scientist: Logics, AI, and Politics." The papers are organized in four parts on logic and deduction, applications of logic, formal methods and security, and agents and planning.

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