Chapters
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Chapter 1
- A review of adaptive Systems and Information Theory
- Problem formulation: Wiener filtering
- The adaptive linear combiner
- Descriptors of Information Theory: entropy and divergence
- Source Coding Theorem
Chapter 2
- Adaptive Information filtering
- Definition of Renyi's entropy
- Renyi's Nonparametric estimators and properties Mean and variance
- Information potential and forces
- Divergence measures Quadratic distances
- Information potential and forces in the joint space
- Fast Computation of IP and CIP
Chapter 3
- Algorithms for Adaptive Information Filtering
- Error entropy criterion
- Algorithms for adaptation
- Minimum error entropy (MEE)
- Recursive information potential MEE
- Stochastic Information Gradient
- Normalized MEE
- Fixed point MEE
- Adaptation of linear filters with divergence
- Backpropagation of information forces
- Fast Renyi's entropy calculations
Chapter 4
- Supervised Applications of Information Theoretic Learning
- Supervised applications of ITL
- MEE and M estimation
- Noise robust properties of MEE
- Nonlinear system identification
- Nonlinear channel equalization
- Feature extraction with ITL
- Classification with ITL
Chapter 5
- Unsupervised learning with ITL
- Clustering evaluation function
- Differential entropy clustering
- Clustering algorithm based on cross information potential
- Information Theoretic Clustering
- A novel principle for unsupervised learning
- Hebbian learning and maximum entropy
- Blind deconvolution with ITL
- Independent component analysis with ITL
Chapter 6
- ITL and Kernel Methods
- Definition of RKHS
- Information Potential as a central moment of the projected data
- Interpretation of SVMs in ITL terms
- A RKHS for ITL
- Adaptive Algorithms in RKHS: KLMS, KRLS, KAPA
Chapter 7
- Generalized Similarity Measures in RKHS
- Definition of Correntropy and its Applications
- Definition of the Correntropy RKHS
- Correntropy Matched Filters
- Correntropy Wiener Filters
- Correntropy Principal Component Analysis
- Other Correntropy based Algorithms
