Description
Taylor and Francis Ltd Entropy Randomization in Machine Learning 1st Edition 2022 Hardbound by Popkov, Yuri S.
A systematic presentation of the randomized machine learning problem: from data processing, through structuring randomized models and algorithmic procedure, to the solution of applications-relevant problems in different fields.Provides new numerical methods for random global optimization and computation of multidimensional integrals.A universal algorithm for randomized machine learning. Preface1. General Concept of Machine Learning2. Data Sources and Models Chapter3. Dimension Reduction Methods4. Randomized Parametric Models5. Entropy-robust Estimation Procedures for Randomized Models and Measurement Noises6. Entropy-Robust Estimation Methods for Probabilities of Belonging in Machine Learning Procedures7. Computational Methods od Randomized Machine Learning 8. Generation Methods for Random Vectors with Given Probability Density Functions over Compact Sets9. Information Technologies of Randomized Machine Learning10. Entropy Classification11. Randomized Machine Learning in Problems of Dynamic Regression and PredictionAppendix A: Maximum Entropy Estimate (MEE) and Its Asymptotic EfficiencyAppendix B: Approximate Estimation of Structural Characteristics of Linear Dynamic Regression Model (LDR)Bibliography