06859cam a2200793 i 4500
ocn878051089
OCoLC
20171030134723.0
m o d
cr |||||||||||
140422s2014 nju ob 001 0 eng
2014016123
9781118914540
(ePub)
1118914546
(ePub)
9781118914557
(Adobe PDF)
1118914554
(Adobe PDF)
9781118914564
1118914562
9781118315231
(hardback)
1118315235
(hardback)
CHBIS
010259798
CHVBK
325941033
DEBBG
BV042487511
NZ1
15920915
DEBSZ
431744548
DEBSZ
434829099
DEBSZ
449440737
AU@
000052794990
(OCoLC)878051089
(OCoLC)891397879
(OCoLC)903257921
(OCoLC)913462197
(OCoLC)927509113
(OCoLC)961585666
(OCoLC)962644700
CL0500000553
Safari Books Online
DLC
eng
rda
pn
DLC
YDX
N$T
EBLCP
YDXCP
OCLCF
DG1
UMI
E7B
OCLCO
COO
DEBBG
OCLCQ
DEBSZ
B24X7
VT2
DG1
pcc
MAIN
TK7882.P3
Zsym
COM
000000
bisacsh
006.4
23
TEC015000
COM016000
COM021030
bisacsh
Kuncheva, Ludmila I.
(Ludmila Ilieva),
1959-
Combining pattern classifiers : methods and algorithms /
Ludmila I. Kuncheva.
[electronic resource]
Second edition.
Hoboken, NJ :
Wiley,
2014.
1 online resource (xxi, 357 pages)
text
txt
rdacontent
computer
c
rdamedia
online resource
cr
rdacarrier
Includes bibliographical references and index.
""Titlepage""; ""Copyright""; ""Dedication""; ""Preface""; ""The Playing Field""; ""Software""; ""Structure and What is New in the Second Edition""; ""Who is This Book For?""; ""Notes""; ""Acknowledgements""; ""1 Fundamentals of Pattern Recognition""; ""1.1 Basic Concepts: Class, Feature, Data Set""; ""1.2 Classifier, Discriminant Functions, Classification Regions""; ""1.3 Classification Error and Classification Accuracy""; ""1.4 Experimental Comparison of Classifiers""; ""1.5 Bayes Decision Theory""; ""1.6 Clustering and Feature Selection""; ""1.7 Challenges of Real-Life Data""; ""Appendix""
""1.A.1 Data Generation""""1.A.2 Comparison of Classifiers""; ""1.A.3 Feature Selection""; ""Notes""; ""2 Base Classifiers""; ""2.1 Linear and Quadratic Classifiers""; ""2.2 Decision Tree Classifiers""; ""2.3 The NaÃv̄e Bayes Classifier""; ""2.4 Neural Networks""; ""2.5 Support Vector Machines""; ""2.6 The k-Nearest Neighbor Classifier (k-nn)""; ""2.7 Final Remarks""; ""Appendix""; ""2.A.1 Matlab Code for the Fish Data""; ""2.A.2 Matlab Code for Individual Classifiers""; ""Notes""; ""3 An Overview of the Field""; ""3.1 Philosophy""; ""3.2 Two Examples""; ""3.3 Structure of the Area""
880-01
""5.3 Nontrainable (Fixed) Combination Rules""""5.4 The Weighted Average (Linear Combiner)""; ""5.5 A Classifier as a Combiner""; ""5.6 An Example of Nine Combiners for Continuous-Valued Outputs""; ""5.7 To Train or Not to Train?""; ""Appendix""; ""5.A.1 Theoretical Classification Error for the Simple Combiners""; ""5.A.2 Selected Matlab Code""; ""Notes""; ""6 Ensemble Methods""; ""6.1 Bagging""; ""6.2 Random Forests""; ""6.3 Adaboost""; ""6.4 Random Subspace Ensembles""; ""6.5 Rotation Forest""; ""6.6 Random Linear Oracle""; ""6.7 Error Correcting Output Codes (ECOC)""; ""Appendix""
""6.A.1 Bagging""""6.A.2 AdaBoost""; ""6.A.3 Random Subspace""; ""6.A.4 Rotation Forest""; ""6.A.5 Random Linear Oracle""; ""6.A.6 Ecoc""; ""Notes""; ""7 Classifier Selection""; ""7.1 Preliminaries""; ""7.2 Why Classifier Selection Works""; ""7.3 Estimating Local Competence Dynamically""; ""7.4 Pre-Estimation of the Competence Regions""; ""7.5 Simultaneous Training of Regions and Classifiers""; ""7.6 Cascade Classifiers""; ""Appendix: Selected Matlab Code""; ""7.A.1 Banana Data""; ""7.A.2 Evolutionary Algorithm for a Selection Ensemble for the Banana Data""
"Combined classifiers, which are central to the ubiquitous performance of pattern recognition and machine learning, are generally considered more accurate than single classifiers. In a didactic, detailed assessment, Combining Pattern Classifiers examines the basic theories and tactics of classifier combination while presenting the most recent research in the field. Among the pattern recognition tasks that this book explores are mail sorting, face recognition, signature verification, decoding brain fMRI images, identifying emotions, analyzing gene microarray data, and spotting patterns in consumer preference. This updated second edition is equipped with the latest knowledge for academics, students, and practitioners involved in pattern recognition fields"--
Provided by publisher.
"Classifier Combination is a field of growing interest within the very large area of Pattern Classification"--
Provided by publisher.
Print version record and CIP data provided by publisher.
Pattern recognition systems.
Image processing
Digital techniques.
TECHNOLOGY & ENGINEERING
Imaging Systems.
bisacsh
COMPUTERS
Computer Vision & Pattern Recognition.
bisacsh
COMPUTERS
Database Management
Data Mining.
bisacsh
Image processing
Digital techniques.
fast
(OCoLC)fst00967508
Pattern recognition systems.
fast
(OCoLC)fst01055266
COMPUTERS / Computer Vision & Pattern Recognition.
Electronic books.
Electronic books.
Print version:
Kuncheva, Ludmila I. (Ludmila Ilieva), 1959-
Combining pattern classifiers.
Second edition.
Hoboken, New Jersey : Wiley, [2014]
9781118315231
(DLC) 2014014214
(OCoLC)878050954
http://onlinelibrary.wiley.com/book/10.1002/9781118914564
Wiley Online Library
505-01/Zsym
""3.4 Quo Vadis?""""Notes""; ""4 Combining Label Outputs""; ""4.1 Types of Classifier Outputs""; ""4.2 A Probabilistic Framework for Combining Label Outputs""; ""4.3 Majority Vote""; ""4.4 Weighted Majority Vote""; ""4.5 Na�ve-Bayes Combiner""; ""4.6 Multinomial Methods""; ""4.7 Comparison of Combination Methods for Label Outputs""; ""Appendix""; ""4.A.1 Matan’s Proof for the Limits on the Majority Vote Accuracy""; ""4.A.2 Selected Matlab Code""; ""Notes""; ""5 Combining Continuous-Valued Outputs""; ""5.1 Decision Profile""; ""5.2 How Do We Get Probability Outputs?""
ddc
BK
207387
207387