The stability-plasticity dilemma

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Table of contents

Abstract
Acknowledgements
List of Figures
List of Tables
1 Introduction
2 The Problem
2.1 Classical Supervised Machine Learning
2.1.1 Scenario
2.1.2 Performance criterion
2.1.3 Learning systems as optimization tools
2.1.4 The bias-variance tradeoff
2.1.5 Overfitting
2.1.6 Practical evaluation measures
2.2 Data Streaming
2.2.1 Practical challenges
2.2.2 Theoretical challenges
2.2.3 Concept change
2.2.4 Types of concept change
2.2.5 Properties of concept change
2.2.6 The stability-plasticity dilemma
2.2.7 Adaptation and anticipation
2.3 Online Machine Learning
2.3.1 Scenario
2.3.2 Practical evaluation measures
2.3.3 The theory of online learning
2.3.4 Online learning in practice
2.3.5 Online learning datasets
2.4 Summary
3 State of Art
3.1 Adapting to the Change
3.1.1 Explicit detection
3.1.2 Implicit adaptation
3.2 Online Classifiers
3.2.1 IB3 (1991)
3.2.2 FLORA (1996)
3.2.3 RePro (2005)
3.2.4 PreDet (2008)
3.3 Online Ensembles of Classifiers
3.3.1 DWM (2003)
3.3.2 CDC (2003)
3.3.3 KBS-stream (2005)
3.3.4 DIC (2008)
3.3.5 Adwin Bagging (2009)
3.3.6 ASHT-Bagging (2009)
3.3.7 CCP (2010)
3.3.8 Leveraging Bagging (2010)
3.3.9 DDD (2012)
3.4 Summary
4 Adaptation to Concept Changes
4.1 Motivation
4.2 Framework
4.2.1 Experts
4.2.2 Prediction
4.2.3 Weighting functions
4.2.4 Deletion strategies
4.3 DACC
4.3.1 The committee of predictors
4.3.2 The committee evolution
4.3.3 The weighting functions
4.3.4 The final prediction
4.3.5 Processing training examples
4.3.6 Time & memory constraints
4.3.7 Computational complexity
4.3.8 Implicit diversity levels
4.3.9 The stability-plasticity dilemma
4.3.10 Effect of parameters
4.3.11 Choice of parameters
4.4 DACC: Comparison with Other Systems
4.4.1 DACC vs CDC
4.4.2 DACC vs DDD, EDDM, DWM
4.4.3 DACC vs others systems
4.5 Contribution
5 Anticipating Concept Changes
5.1 Concept Predictability
5.1.1 DACCv1
5.1.2 DACCv2
5.1.3 DACCv3
5.2 Concept Reccurence
5.2.1 DACCv4
5.3 ADACC
5.3.1 Computational complexity
5.3.2 Empirical results (1)
5.3.3 Empirical results (2)
5.4 Contribution
6 Conclusion and Perspectives
6.1 DACC
6.1.1 Methodology
6.1.2 Properties
6.1.3 Strengths, weaknesses and perspectives
6.2 ADACC
6.2.1 Methodology
6.2.2 Properties
6.2.3 Strengths, weaknesses and perspectives
6.3 Links with the Theory of Online Learning
6.4 Links with Domain Adaptation and Transfer Learning