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The Resource Introduction to statistical pattern recognition, Keinosuke Fukunaga, (electronic resource)
Introduction to statistical pattern recognition, Keinosuke Fukunaga, (electronic resource)
Resource Information
The item Introduction to statistical pattern recognition, Keinosuke Fukunaga, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.This item is available to borrow from all library branches.
Resource Information
The item Introduction to statistical pattern recognition, Keinosuke Fukunaga, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.
This item is available to borrow from all library branches.
 Summary
 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 chapte
 Language

 eng
 eng
 Edition
 2nd ed.
 Extent
 1 online resource (616 p.)
 Note
 Description based upon print version of record
 Contents

 Cover; Frontmatter; Half Title Page; Title Page; Copyright; Dedication; Table of Contents; Preface; Acknowledgments; Chapter 1: Introduction; 1.1 Formulation of Pattern Recognition Problems; 1.2 Process of Classifier Design; Notation; References; Chapter 2: Random Vectors and Their Properties; 2.1 Random Vectors and Their Distributions; 2.2 Estimation of Parameters; 2.3 Linear Transformation; 2.4 Various Properties of Eigenvalues and Eigenvectors; Computer Projects; Problems; References; Chapter 3: Hypothesis Testing; 3.1 Hypothesis Tests for Two Classes; 3.2 Other Hypothesis Tests
 3.3 Error Probability in Hypothesis Testing3.4 Upper Bounds on the Bayes Error; 3.5 Sequential Hypothesis Testing; Computer Projects; Problems; References; Chapter 4: Parametric Classifiers; 4.1 The Bayes Linear Classifier; 4.2 Linear Classifier Design; 4.3 Quadratic Classifier Design; 4.4 Other Classifiers; Computer Projects; Problems; References; Chapter 5: Parameter Estimation; 5.1 Effect of Sample Size in Estimation; 5.2 Estimation of Classification Errors; 5.3 Holdout, LeaveOneOut, and Resubstitution Methods; 5.4 Bootstrap Methods; Computer Projects; Problems; References
 Chapter 6: Nonparametric Density Estimation6.1 Parzen Density Estimate; 6.2 k Nearest Neighbor Density Estimate; 6.3 Expansion by Basis Functions; Computer Projects; Problems; References; Chapter 7: Nonparametric Classification and Error Estimation; 7.1 General Discussion; 7.2 Voting kNN Procedure  Asymptotic Analysis; 7.3 Voting kNN Procedure  Finite Sample Analysis; 7.4 Error Estimation; 7.5 Miscellaneous Topics in the kNN Approach; Computer Projects; Problems; References; Chapter 8: Successive Parameter Estimation; 8.1 Successive Adjustment of a Linear Classifier
 8.2 Stochastic Approximation8.3 Successive Bayes Estimation; Computer Projects; Problems; References; Chapter 9: Feature Extraction and Linear Mapping for Signal Representation; 9.1 The Discrete KarhunenLoéve Expansion; 9.2 The KarhunenLoéve Expansion for Random Processes; 9.3 Estimation of Eigenvalues and Eigenvectors; Computer Projects; Problems; References; Chapter 10: Feature Extraction and Linear Mapping for Classification; 10.1 General Problem Formulation; 10.2 Discriminant Analysis; 10.3 Generalized Criteria; 10.4 Nonparametric Discriminant Analysis
 10.5 Sequential Selection of Quadratic Features10.5 Feature Subset Selection; Computer Projects; Problems; References; Chapter 11: Clustering; 11.1 Parametric Clustering; 11.2 Nonparametric Clustering; 11.3 Selection of Representatives; Computer Projects; Problems; References; Backmatter; Appendix A: Derivatives of Matrices; Appendix B: Mathematical Formulas; Appendix C: Normal Error Table; Appendix D: Gamma Function Table; Index; About the Author; Back Cover
 Isbn
 9786611050382
 Label
 Introduction to statistical pattern recognition
 Title
 Introduction to statistical pattern recognition
 Statement of responsibility
 Keinosuke Fukunaga
 Language

 eng
 eng
 Summary
 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 chapte
 Cataloging source
 MiAaPQ
 http://library.link/vocab/creatorName
 Fukunaga, Keinosuke
 Dewey number

 006.4
 006.4 20
 Illustrations
 illustrations
 Index
 index present
 Language note
 English
 LC call number
 Q327
 LC item number
 .F85 1990
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 Series statement
 Computer science and scientific computing
 http://library.link/vocab/subjectName

 Pattern perception
 Decision making
 Mathematical statistics
 Label
 Introduction to statistical pattern recognition, Keinosuke Fukunaga, (electronic resource)
 Note
 Description based upon print version of record
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code
 cr
 Content category
 text
 Content type code
 txt
 Contents

 Cover; Frontmatter; Half Title Page; Title Page; Copyright; Dedication; Table of Contents; Preface; Acknowledgments; Chapter 1: Introduction; 1.1 Formulation of Pattern Recognition Problems; 1.2 Process of Classifier Design; Notation; References; Chapter 2: Random Vectors and Their Properties; 2.1 Random Vectors and Their Distributions; 2.2 Estimation of Parameters; 2.3 Linear Transformation; 2.4 Various Properties of Eigenvalues and Eigenvectors; Computer Projects; Problems; References; Chapter 3: Hypothesis Testing; 3.1 Hypothesis Tests for Two Classes; 3.2 Other Hypothesis Tests
 3.3 Error Probability in Hypothesis Testing3.4 Upper Bounds on the Bayes Error; 3.5 Sequential Hypothesis Testing; Computer Projects; Problems; References; Chapter 4: Parametric Classifiers; 4.1 The Bayes Linear Classifier; 4.2 Linear Classifier Design; 4.3 Quadratic Classifier Design; 4.4 Other Classifiers; Computer Projects; Problems; References; Chapter 5: Parameter Estimation; 5.1 Effect of Sample Size in Estimation; 5.2 Estimation of Classification Errors; 5.3 Holdout, LeaveOneOut, and Resubstitution Methods; 5.4 Bootstrap Methods; Computer Projects; Problems; References
 Chapter 6: Nonparametric Density Estimation6.1 Parzen Density Estimate; 6.2 k Nearest Neighbor Density Estimate; 6.3 Expansion by Basis Functions; Computer Projects; Problems; References; Chapter 7: Nonparametric Classification and Error Estimation; 7.1 General Discussion; 7.2 Voting kNN Procedure  Asymptotic Analysis; 7.3 Voting kNN Procedure  Finite Sample Analysis; 7.4 Error Estimation; 7.5 Miscellaneous Topics in the kNN Approach; Computer Projects; Problems; References; Chapter 8: Successive Parameter Estimation; 8.1 Successive Adjustment of a Linear Classifier
 8.2 Stochastic Approximation8.3 Successive Bayes Estimation; Computer Projects; Problems; References; Chapter 9: Feature Extraction and Linear Mapping for Signal Representation; 9.1 The Discrete KarhunenLoéve Expansion; 9.2 The KarhunenLoéve Expansion for Random Processes; 9.3 Estimation of Eigenvalues and Eigenvectors; Computer Projects; Problems; References; Chapter 10: Feature Extraction and Linear Mapping for Classification; 10.1 General Problem Formulation; 10.2 Discriminant Analysis; 10.3 Generalized Criteria; 10.4 Nonparametric Discriminant Analysis
 10.5 Sequential Selection of Quadratic Features10.5 Feature Subset Selection; Computer Projects; Problems; References; Chapter 11: Clustering; 11.1 Parametric Clustering; 11.2 Nonparametric Clustering; 11.3 Selection of Representatives; Computer Projects; Problems; References; Backmatter; Appendix A: Derivatives of Matrices; Appendix B: Mathematical Formulas; Appendix C: Normal Error Table; Appendix D: Gamma Function Table; Index; About the Author; Back Cover
 Dimensions
 unknown
 Edition
 2nd ed.
 Extent
 1 online resource (616 p.)
 Form of item
 online
 Isbn
 9786611050382
 Media category
 computer
 Media type code
 c
 Specific material designation
 remote
 System control number

 (EBL)294219
 (OCoLC)476057325
 (SSID)ssj0000182931
 (PQKBManifestationID)12001984
 (PQKBTitleCode)TC0000182931
 (PQKBWorkID)10173095
 (PQKB)10292033
 (MiAaPQ)EBC294219
 (EXLCZ)991000000000009942
 Label
 Introduction to statistical pattern recognition, Keinosuke Fukunaga, (electronic resource)
 Note
 Description based upon print version of record
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code
 cr
 Content category
 text
 Content type code
 txt
 Contents

 Cover; Frontmatter; Half Title Page; Title Page; Copyright; Dedication; Table of Contents; Preface; Acknowledgments; Chapter 1: Introduction; 1.1 Formulation of Pattern Recognition Problems; 1.2 Process of Classifier Design; Notation; References; Chapter 2: Random Vectors and Their Properties; 2.1 Random Vectors and Their Distributions; 2.2 Estimation of Parameters; 2.3 Linear Transformation; 2.4 Various Properties of Eigenvalues and Eigenvectors; Computer Projects; Problems; References; Chapter 3: Hypothesis Testing; 3.1 Hypothesis Tests for Two Classes; 3.2 Other Hypothesis Tests
 3.3 Error Probability in Hypothesis Testing3.4 Upper Bounds on the Bayes Error; 3.5 Sequential Hypothesis Testing; Computer Projects; Problems; References; Chapter 4: Parametric Classifiers; 4.1 The Bayes Linear Classifier; 4.2 Linear Classifier Design; 4.3 Quadratic Classifier Design; 4.4 Other Classifiers; Computer Projects; Problems; References; Chapter 5: Parameter Estimation; 5.1 Effect of Sample Size in Estimation; 5.2 Estimation of Classification Errors; 5.3 Holdout, LeaveOneOut, and Resubstitution Methods; 5.4 Bootstrap Methods; Computer Projects; Problems; References
 Chapter 6: Nonparametric Density Estimation6.1 Parzen Density Estimate; 6.2 k Nearest Neighbor Density Estimate; 6.3 Expansion by Basis Functions; Computer Projects; Problems; References; Chapter 7: Nonparametric Classification and Error Estimation; 7.1 General Discussion; 7.2 Voting kNN Procedure  Asymptotic Analysis; 7.3 Voting kNN Procedure  Finite Sample Analysis; 7.4 Error Estimation; 7.5 Miscellaneous Topics in the kNN Approach; Computer Projects; Problems; References; Chapter 8: Successive Parameter Estimation; 8.1 Successive Adjustment of a Linear Classifier
 8.2 Stochastic Approximation8.3 Successive Bayes Estimation; Computer Projects; Problems; References; Chapter 9: Feature Extraction and Linear Mapping for Signal Representation; 9.1 The Discrete KarhunenLoéve Expansion; 9.2 The KarhunenLoéve Expansion for Random Processes; 9.3 Estimation of Eigenvalues and Eigenvectors; Computer Projects; Problems; References; Chapter 10: Feature Extraction and Linear Mapping for Classification; 10.1 General Problem Formulation; 10.2 Discriminant Analysis; 10.3 Generalized Criteria; 10.4 Nonparametric Discriminant Analysis
 10.5 Sequential Selection of Quadratic Features10.5 Feature Subset Selection; Computer Projects; Problems; References; Chapter 11: Clustering; 11.1 Parametric Clustering; 11.2 Nonparametric Clustering; 11.3 Selection of Representatives; Computer Projects; Problems; References; Backmatter; Appendix A: Derivatives of Matrices; Appendix B: Mathematical Formulas; Appendix C: Normal Error Table; Appendix D: Gamma Function Table; Index; About the Author; Back Cover
 Dimensions
 unknown
 Edition
 2nd ed.
 Extent
 1 online resource (616 p.)
 Form of item
 online
 Isbn
 9786611050382
 Media category
 computer
 Media type code
 c
 Specific material designation
 remote
 System control number

 (EBL)294219
 (OCoLC)476057325
 (SSID)ssj0000182931
 (PQKBManifestationID)12001984
 (PQKBTitleCode)TC0000182931
 (PQKBWorkID)10173095
 (PQKB)10292033
 (MiAaPQ)EBC294219
 (EXLCZ)991000000000009942
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