Entropy Randomization in Machine Learning (Paperback, 1)

Preface

1. General Concept of Machine Learning

2. Data Sources and Models Chapter

3. Dimension Reduction Methods

4. Randomized Parametric Models

5. Entropy-robust Estimation Procedures for Randomized Models and Measurement Noises

6. Entropy-Robust Estimation Methods for Probabilities of Belonging in Machine Learning Procedures

7. Computational Methods od Randomized Machine Learning 

8. Generation Methods for Random Vectors with Given Probability Density Functions over Compact Sets

9. Information Technologies of Randomized Machine Learning

10. Entropy Classification

11. Randomized Machine Learning in Problems of Dynamic Regression and Prediction

Appendix A: Maximum Entropy Estimate (MEE) and Its Asymptotic Efficiency

Appendix B: Approximate Estimation of Structural Characteristics of Linear Dynamic Regression Model (LDR)

Bibliography