Statistical Machine Learning
-10%
portes grátis
Statistical Machine Learning
A Unified Framework
Golden, Richard
Taylor & Francis Ltd
07/2020
506
Dura
Inglês
9781138484696
15 a 20 dias
1130
Descrição não disponível.
Part I: Inference and Learning Machines. 1. A Statistical Machine Learning Framework 2. Set Theory for Concept Modeling 3. Formal Machine Learning Algorithms Part II: Deterministic Learning Machines 4. Linear Algebra for Machine Learning 5. Matrix Calculus for Machine Learning 6. Convergence of Time-Invariant Dynamical Systems 7. Batch Learning Algorithm Convergence Part III: Stochastic Learning Machines 8. Random Vectors and Random Functions 9. Stochastic Sequences 10. Probability Models of Data Generation 11. Monte Carlo Markov Chain Algorithm Convergence 12. Adaptive Learning Algorithm Convergence Part IV: Generalization Performance 13. Statistical Learning Objective Function Design 14. Simulation Methods for Evaluating Generalization 15. Analytic Formulas for Evaluating Generalization 16. Model Selection and Evaluation
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Information Matrix Equality;Gradient Descent Direction;artificial intelligence;Strong Wolfe Conditions;algorithms;Monte Carlo Bootstrap;random vectors;Empirical Risk Function;vector calculus;Stochastic Sequence;MCMC Algorithm;Strict Local Minimizer;Data Set;Support Specification Measure;Continuous Time Dynamical System;Discrete Time Dynamical System;Finite State Space;Iterated Map;Data Generating Process;Overfitting Phenomenon;MCMC Sampling Algorithm;Bootstrap Data Sets;ICM;State Transition Graph;Lyapunov Function;MSC;Equilibrium Points;ICM Algorithm
Part I: Inference and Learning Machines. 1. A Statistical Machine Learning Framework 2. Set Theory for Concept Modeling 3. Formal Machine Learning Algorithms Part II: Deterministic Learning Machines 4. Linear Algebra for Machine Learning 5. Matrix Calculus for Machine Learning 6. Convergence of Time-Invariant Dynamical Systems 7. Batch Learning Algorithm Convergence Part III: Stochastic Learning Machines 8. Random Vectors and Random Functions 9. Stochastic Sequences 10. Probability Models of Data Generation 11. Monte Carlo Markov Chain Algorithm Convergence 12. Adaptive Learning Algorithm Convergence Part IV: Generalization Performance 13. Statistical Learning Objective Function Design 14. Simulation Methods for Evaluating Generalization 15. Analytic Formulas for Evaluating Generalization 16. Model Selection and Evaluation
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Information Matrix Equality;Gradient Descent Direction;artificial intelligence;Strong Wolfe Conditions;algorithms;Monte Carlo Bootstrap;random vectors;Empirical Risk Function;vector calculus;Stochastic Sequence;MCMC Algorithm;Strict Local Minimizer;Data Set;Support Specification Measure;Continuous Time Dynamical System;Discrete Time Dynamical System;Finite State Space;Iterated Map;Data Generating Process;Overfitting Phenomenon;MCMC Sampling Algorithm;Bootstrap Data Sets;ICM;State Transition Graph;Lyapunov Function;MSC;Equilibrium Points;ICM Algorithm
