Improving Classifier Generalization: Real-Time Machine Learning Based Applications
Title Statements
- Full title :Improving Classifier Generalization : Real-Time Machine Learning Based Applications.
Edition Statements
- Publication Date:©2023.
Publication Statements
- Publisher(s):Springer
- Place of Publication:Singapore
- Language:eng
- Publication Date:©2023.
The Physical Object
- Description:1 online resource (181 pages).
Edition Identifiers
- ISBN:9789811950735, 9811950733
- oclc:on1349946184
- LC Classification:Q325.5-.7
- Dewey Decimal:006.31
Table of Contents
Intro -- Preface -- Contents -- About the Authors -- Acronyms -- Symbols -- List of Figures -- List of Tables -- 1 Introduction -- 1.1 Basics -- 1.2 Statistical Learning Theory and VC Dimension -- 1.2.1 VC Dimension -- 1.2.2 Probably Approximate Correct (PAC) Learning Framework -- 1.2.3 Growth Function -- 1.3 Bias-Variance Trade-Off -- 1.3.1 Occam's Razor -- 1.4 Outline -- References -- 2 Methods Used to Improve Generalization Performance -- 2.1 Maximum Margin Classifier -- 2.1.1 Upper Bound on VC Dimension of Linear Classifiers -- 2.1.2 Support Vector Machine (SVM) -- 2.2 Bagging and Boosting -- 2.3 Semi-Supervised Learning, Active Learning, and Transfer Learning -- 2.4 Regularization -- 2.5 Stochastic Gradient Descent, Momentum, ... -- 2.6 Conclusions -- References -- 3 MVPC-A Classifier with Very Low VC Dimension -- 3.1 Majority Vote Classifier -- 3.1.1 The Majority Vote Point (MVP) Classifier -- 3.1.2 Implementation of MVP Classifier -- 3.2 Evaluating and Comparing VC Dimension -- 3.2.1 Upper Bound on VC Dimension of MVP Classifier -- 3.2.2 Empirical Estimation of VC Dimension -- 3.2.3 Comparing with VC Dimension of Linear Classifiers -- 3.3 Case Studies -- 3.3.1 Rotating Mach…
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