Entropy Randomization in Machine Learning
portes grátis
Entropy Randomization in Machine Learning
Popkov, Alexey Yu.; Popkov, Yuri S.; Dubnov, Yuri A.
Taylor & Francis Ltd
10/2024
392
Mole
9781032307749
Pré-lançamento - envio 15 a 20 dias após a sua edição
Descrição não disponível.
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
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Entropy-Robust Estimation;Machine Learning;Computational Methods;Information Technologies;Probabilities;Procedures;Dynamic Regression;Prediction;Entropy Randomization;Admissible Set;Balance Constraints;Lagrange Multipliers;Measurement Noises;Lagrange Functional;Obtain Optimality Conditions;AR Method;Decision Rule Model;Elementary Cube;Elementary Parallelepipeds;Ml Algorithm;Learning Sample;Ml Procedure;Standard PDF;Gateaux Derivative;Lipschitz Constant;Modern Computer Systems;Random Vectors;Pulse Characteristics;High Dimensional Random Vectors;Entropy Classification;Decision Tree Design;Address Space;Rpm;SVM
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
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Entropy-Robust Estimation;Machine Learning;Computational Methods;Information Technologies;Probabilities;Procedures;Dynamic Regression;Prediction;Entropy Randomization;Admissible Set;Balance Constraints;Lagrange Multipliers;Measurement Noises;Lagrange Functional;Obtain Optimality Conditions;AR Method;Decision Rule Model;Elementary Cube;Elementary Parallelepipeds;Ml Algorithm;Learning Sample;Ml Procedure;Standard PDF;Gateaux Derivative;Lipschitz Constant;Modern Computer Systems;Random Vectors;Pulse Characteristics;High Dimensional Random Vectors;Entropy Classification;Decision Tree Design;Address Space;Rpm;SVM
