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.
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About the Author
By Keinosuke Fukunaga
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29 of 30 people found the following review helpful:
4.0 out of 5 stars
pattern recognition in engineering,
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This review is from: Introduction to Statistical Pattern Recognition, Second Edition (Computer Science & Scientific Computing) (Hardcover)
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 (1973) for good explanations of the fundamental concepts. The second edition that was recent ly published with Stark as a third author is also highly recommended. For statisticians McLachlan's book is now far and away the best source.
16 of 18 people found the following review helpful:
3.0 out of 5 stars
A best book on Statistical Pattern Recognition,
By supercutepig (USA) - See all my reviews
This review is from: Introduction to Statistical Pattern Recognition, Second Edition (Computer Science & Scientific Computing) (Hardcover)
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.
10 of 11 people found the following review helpful:
5.0 out of 5 stars
Standard Reference in the Field,
By A Customer
This review is from: Introduction to Statistical Pattern Recognition, Second Edition (Computer Science & Scientific Computing) (Hardcover)
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.
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Inside This Book
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First Sentence:
This book presents and discusses the fundamental mathematical tools for statistical decision-making processes in pattern recognition. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
invariant under any orthonormal transformation, reclassification algorithm, optimum linear classifier, uniform kernel function, likelihood ratio classifier, fixed increment rule, linear classifier design, correlation classifier, normal kernel function, quadratic classifier, toeplitz approximation, expected vector, priori density function, grouped estimate, piecewise linear classifier, class separability, kernel covariance, holdout method, two covariance matrices, misclassified samples, whitening transformation, minimax test, scatter matrices, reject region, intrinsic dimensionality Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Repeat Experiment, Pattern Anal, Computer Projects, Machine Intell, Estimation of the Parzen, Mean Standard, Englewood Cliffs, Academic Press, New Jersey, Electronic Computers, Iteration Input, Modulation Theory, Repeat Example, Repeat Project, Second Edition, Stochastic Processes New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
This book presents and discusses the fundamental mathematical tools for statistical decision-making processes in pattern recognition. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
invariant under any orthonormal transformation, reclassification algorithm, optimum linear classifier, uniform kernel function, likelihood ratio classifier, fixed increment rule, linear classifier design, correlation classifier, normal kernel function, quadratic classifier, toeplitz approximation, expected vector, priori density function, grouped estimate, piecewise linear classifier, class separability, kernel covariance, holdout method, two covariance matrices, misclassified samples, whitening transformation, minimax test, scatter matrices, reject region, intrinsic dimensionality Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Repeat Experiment, Pattern Anal, Computer Projects, Machine Intell, Estimation of the Parzen, Mean Standard, Englewood Cliffs, Academic Press, New Jersey, Electronic Computers, Iteration Input, Modulation Theory, Repeat Example, Repeat Project, Second Edition, Stochastic Processes New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 21
books:
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7
books
cite this book:
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- Probability, Random Variables and Stochastic Processes with Errata Sheet by Athanasios Papoulis on page 51, and page 124
- Applied Linear Algebra (3rd Edition) by Ben Noble on page 51, and page 441
- Matrix Algebra Useful for Statistics (Wiley Series in Probability and Statistics) by Shayle R. Searle on page 51, and Back Matter
- The Algebraic Eigenvalue Problem (Monographs on Numerical Analysis) by the late J. H. Wilkinson on page 51, and page 441
- Artificl Intell Instructrs Manual Tx by Author Unknown on page 508
See all 21 books this book cites
- Topics in Artificial Intelligence: 5th Catalonian Conference on AI, CCIA 2002, Castellón, Spain, October 24-25, 2002. Proceedings (Lecture Notes in ... / Lecture Notes in Artificial Intelligence) by Maria Teresa Escrig Monferrer on page 206
- AI 2002: Advances in Artificial Intelligence by Bob McKay on page 166
- Computational Statistics Handbook with MATLAB (Chapman & Hall/CRC Computer Science & Data Analysis) by Wendy L. Martinez in Back Matter
- Computers and Games: Third International Conference, CG 2002, Edmonton, Canada, July 25-27, 2002, Revised Papers (Lecture Notes in Computer Science) by Jonathan Schaeffer on page 412
- Intelligent Control Systems Using Soft Computing Methodologies by Ali Zilouchian on page 92
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