Pattern classification:
Gespeichert in:
| Vorheriger Titel: | Duda, Richard O. Pattern classification and scene analysis |
|---|---|
| Beteiligte Personen: | , , |
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
New York [u.a.]
Wiley
2001
|
| Ausgabe: | 2. ed. |
| Schriftenreihe: | A Wiley-interscience publication
|
| Schlagwörter: | |
| Links: | http://www3.ub.tu-berlin.de/ihv/000793669.pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008943047&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008943047&sequence=000002&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Erg. bildet: Stork, David G.: Computer manual in MATLAB to accompany Pattern Classification, second edition |
| Umfang: | xx, 654 Seiten Illustrationen, Diagramme |
| ISBN: | 0471056693 9780471056690 |
Internformat
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| 250 | |a 2. ed. | ||
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| adam_text | CONTENTS
PREFACE
XVII
INTRODUCTION
1.1
1.2
1.2.1
1.3
1.3.1
1.3.2
1.3.3
1.3.4
1.3.5
1.4
1.4.1
1.4.2
1.4.3
1.4.4
1.4.5
1.4.6
1.5
1.5.1
1.5.2
1.5.3
1.6
Summary by Chapters,
Bibliographical and Historical Remarks,
Bibliography,
BAYESIAN DECISION THEORY
20
2.1
2.2
2.2.1
2.3
*2.3.1 Minimax Criterion,
VII
viil CONTENTS
*2.3.2 Neyman-Pearson Criterion,
2.4
2.4.1
2.4.2
2.5
2.5.1
2.5.2
2.6
2.6.1
2.6.2
2.6.3
Example
Gaussian Data,
*2.7 Error Probabilities and Integrals,
*2.8 Error Bounds for Normal Densities,
2.8.1
2.8.2
Example
2.8.3
2.9
2.9.1
Example
Binary Data,
*2.10 Missing and Noisy Features,
2.10.1
2.10.2
*2.11 Bayesian Belief Networks,
Example
*2.12 Compound Bayesian Decision Theory and Context,
Summary,
Bibliographical and Historical Remarks,
Problems,
Computer exercises,
Bibliography,
MAXIMUM-LIKELIHOOD AND BAYESIAN
PARAMETER ESTIMATION
84
3.1
3.2
3.2.1
3.2.2
3.2.3
3.2.4
3.3
3.3.1
3.3.2
3.4
3.4.1
3.4.2
3.4.3
CONTENTS iX
3.5 Bayesian Parameter
Example I Recursive
3.5.1
3.5.2
3.5.3
*3.6 Sufficient Statistics,
3.6.1
3.7
3.7.1
3.7.2
3.7.3
*3.8 Component Analysis and Discriminants,
3.8.1
3.8.2
3.8.3
*3.9 Expectation-Maximization (EM),
Example
3.10
3.10.1
3.10.2
3.10.3
3.10.4
Example
3.10.5
Example
3.10.6
Summary,
Bibliographical and Historical Remarks,
Problems,
Computer exercises,
Bibliography,
NONPARAMETRIC TECHNIQUES
4.1
4.2
4.3 Parzen
4.3.1
4.3.2
4.3.3
4.3.4
4.3.5
4.3.6
4.4
4.4.1
4.4.2
4.5
4.5.1
4.5.2
4.5.3
4.5.4
X
4.5.5
4.6
4.6.1
4.6.2
*4.7 Fuzzy Classification,
*4.8 Reduced Coulomb Energy Networks,
4.9
Summary,
Bibliographical and Historical Remarks,
Problems,
Computer exercises,
Bibliography,
LINEAR DISCRIMINANT FUNCTIONS
5.1
5.2
5.2.1
5.2.2
5.3
5.4
5.4.1
5.4.2
5.5
5.5.1
5.5.2
5.5.3
5.6
5.6.1
5.6.2
5.7
5.8
5.8.1
Example
Pseudoinverse,
5.8.2
5.8.3
5.8.4
5.8.5
5.9
5.9.1
5.9.2
5.9.3
5.9.4
*5.10 Linear Programming Algorithms,
5.10.1
5.10.2
5.10.3
*5.11 Support Vector Machines,
5.11.1
Example
CONTENTS
5.12 Multicategory
5.12.1
5.12.2
5.12.3
Summary,
Bibliographical and Historical Remarks,
Problems,
Computer exercises,
Bibliography,
MULTILAYER NEURAL NETWORKS
6.1
6.2
6.2.1
6.2.2
6.3
6.3.1
6.3.2
6.3.3
6.4
6.4.1
6.4.2
6.4.3
6.4.4
6.5
6.5.1
6.6
6.6.1
6.6.2
*6.7 Related Statistical Techniques,
6.8
6.8.1
6.8.2
6.8.3
6.8.4
6.8.5
6.8.6
6.8.7
6.8.8
6.8.9
6.8.10
6.8.11
6.8.12
6.8.13
6.8.14
6.8.15
6.8.16
*6.9 Second-Order Methods,
6.9.1
6.9.2
XII
6.9.3 Quickprop, 320
6.9.4
Example
*6.10 Additional Networks and Training Methods,
6.10.1
6.10.2
6.10.3
6.10.4
6.10.5
6.10.6
6.11
Summary,
Bibliographical and Historical Remarks,
Problems,
Computer exercises,
Bibliography,
STOCHASTIC METHODS
350
7.1
7.2
7.2.1
7.2.2
7.2.3
7.3
7.3.1
7.3.2
7.3.3
7.3.4
*7.4 Boltzmann Networks and Graphical Models,
7.4.1
*7.5 Evolutionary Methods,
7.5.1
7.5.2
7.5.3
*7.6 Genetic Programming,
Summary,
Bibliographical and Historical Remarks,
Problems,
Computer exercises,
Bibliography,
8
394
8.1
8.2
8.3
8.3.1
8.3.2
8.3.3
8.3.4
CONTENTS
8.3.5
Example
8.3.6
8.3.7
8.3.8
8.3.9
8.3.10
Example
8.4
8.4.1
8.4.2
8.4.3
*8.5 Recognition with Strings,
8.5.1
8.5.2
8.5.3
8.5.4
8.5.5
8.6
8.6.1
8.6.2
Example
8.6.3
8.7
Example
*8.8 Rule-Based Methods,
8.8.1
Summary,
Bibliographical and Historical Remarks,
Problems,
Computer exercises,
Bibliography,
ALGORITHM-INDEPENDENT MACHINE LEARNING
9.1
9.2
9.2.1
Example
*9.2.2 Ugly Duckling Theorem,
9.2.3
9.2.4
9.2.5
9.3
9.3.1
9.3.2
9.4
9.4.1
Example
9.4.2
9.5
XIV
9.5.1
9.5.2
9.5.3
9.5.4
9.6
9.6.1
9.6.2
9.6.3
9.6.4
9.6.5
9.6.6
9.6.7
9.6.8
9.7
9.7.1
9.7.2
Summary,
Bibliographical and Historical Remarks,
Problems,
Computer exercises,
Bibliography,
10
517
10.1
10.2
10.3
10.4
10.4.1
10.4.2
10.4.3
* 10.4.4
10.5
10.5.1
10.5.2
Example
10.5.3
10.6
10.6.1
10.7
10.7.1
10.7.2
10.7.3
Example
*10.8 Iterative Optimization,
10.9
10.9.1
10.9.2
10.9.3
10.9.4
♦10.10
CONTENTS
*10.11
10.11.1
10.11.2
10.11.3
*10.12 Graph-Theoretic Methods,
10.13
10.13.1
10.13.2
*10.13.3 Independent Component Analysis (ICA),
10.14
(MDS),
10.14.1
10.14.2
Summary,
Bibliographical and Historical Remarks,
Problems,
Computer exercises,
Bibliography,
MATHEMATICAL FOUNDATIONS
A.1 Notation,
A.2 Linear Algebra,
A.2.1 Notation and Preliminaries,
A.2.2 Inner Product,
A.2.3 Outer Product,
A.2.4 Derivatives of Matrices,
A.2.5 Determinant and Trace,
A.2.6 Matrix Inversion,
A.2.7 Eigenvectors and Eigenvalues,
A.3
A.4 Probability Theory,
A.4.1 Discrete Random Variables,
A.4.2 Expected Values,
A.4.3 Pairs of Discrete Random Variables,
A.4.4 Statistical Independence,
A.4.5 Expected Values of Functions of Two Variables,
A.4.6 Conditional Probability,
A.4.7 The Law of Total Probability and
A.4.8 Vector Random Variables,
A.4.9 Expectations, Mean Vectors and Covariance Matrices,
A.4.10 Continuous Random Variables,
A.4.
A.4.
A.5 Gaussian Derivatives and Integrals,
A.5.1 Multivariate Normal Densities,
A.5.2 Bivariate Normal Densities,
A.6 Hypothesis Testing,
A.6.1 Chi-Squared Test,
A.7 Information Theory,
A.7.
XVI
A.7.2 Relative
A.7.3 Mutual Information, 632
A.8
Bibliography,
INDEX
Practitioners developing or investigating pattern recognition systems in such diverse
application areas as speech recognition, optical character recognition, image pro¬
cessing, or signal analysis, often face the difficult task of having to decide among a
bewildering array of available techniques. This unique text/professional reference
provides the information you need to choose the most appropriate method for a
given class of problems, presenting an in-depth, systematic account of the major top¬
ics in pattern recognition today. A new edition of a classic work that helped define
the field for over a quarter century, this practical book updates and expands the orig¬
inal work, focusing on pattern classification and the immense progress it has experi¬
enced in recent years. Special features include:
•
stochastic methods, genetic algorithms, and theory of learning
•
•
•
•
•
•
•
•
RICHARD
San Jose State University, San Jose, California. PETER E. HART, PhD, is Chief
Executive Officer and President of Ricoh Innovations, Inc. in
DAVID G. STORK, PhD, is Chief Scientist, also at Ricoh Innovations, Inc.
Cover Design: Lynn Cole
WILEY-INTERSCIENCE
John Wiley
Scientific, Technical, and Medical Division
605
New York
Brisbane
|
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| discipline | Informatik Mathematik Wirtschaftswissenschaften |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV013126293 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T10:41:44Z |
| institution | BVB |
| isbn | 0471056693 9780471056690 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-008943047 |
| oclc_num | 264730610 |
| open_access_boolean | |
| owner | DE-945 DE-20 DE-739 DE-29T DE-91 DE-BY-TUM DE-91G DE-BY-TUM DE-703 DE-1102 DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-M347 DE-473 DE-BY-UBG DE-706 DE-861 DE-522 DE-526 DE-634 DE-83 DE-898 DE-BY-UBR DE-11 DE-863 DE-BY-FWS DE-384 DE-2070s DE-1047 DE-B768 |
| owner_facet | DE-945 DE-20 DE-739 DE-29T DE-91 DE-BY-TUM DE-91G DE-BY-TUM DE-703 DE-1102 DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-M347 DE-473 DE-BY-UBG DE-706 DE-861 DE-522 DE-526 DE-634 DE-83 DE-898 DE-BY-UBR DE-11 DE-863 DE-BY-FWS DE-384 DE-2070s DE-1047 DE-B768 |
| physical | xx, 654 Seiten Illustrationen, Diagramme |
| publishDate | 2001 |
| publishDateSearch | 2001 |
| publishDateSort | 2001 |
| publisher | Wiley |
| record_format | marc |
| series2 | A Wiley-interscience publication |
| spellingShingle | Duda, Richard O. Hart, Peter E. 1928- Stork, David G. ca. 20./21. Jh Pattern classification Mustererkennung (DE-588)4040936-3 gnd |
| subject_GND | (DE-588)4040936-3 |
| title | Pattern classification |
| title_auth | Pattern classification |
| title_exact_search | Pattern classification |
| title_full | Pattern classification Richard O. Duda ; Peter E. Hart ; David G. Stork |
| title_fullStr | Pattern classification Richard O. Duda ; Peter E. Hart ; David G. Stork |
| title_full_unstemmed | Pattern classification Richard O. Duda ; Peter E. Hart ; David G. Stork |
| title_old | Duda, Richard O. Pattern classification and scene analysis |
| title_short | Pattern classification |
| title_sort | pattern classification |
| topic | Mustererkennung (DE-588)4040936-3 gnd |
| topic_facet | Mustererkennung |
| url | http://www3.ub.tu-berlin.de/ihv/000793669.pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008943047&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008943047&sequence=000002&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT dudarichardo patternclassification AT hartpetere patternclassification AT storkdavidg patternclassification |
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Teilbibliothek Mathematik & Informatik
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0102 DAT 770f 2001 A 12901 Lageplan 0102 DAT 770f 2001 A 12902 Lageplan 0102 DAT 770f 2001 A 12904 Lageplan |
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