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| Title | Statistical Learning Theory |
| Author | Vladimir N. Vapnik |
| ISBN-13 | 9788126528929 |
| Publication | STAR EXCLUSIVE |
It is particularly germane to this series which is concerned with "Adaptive and Learning Systems", specifically as applied in neural networks, fuzzy systems and artificial intelligence. Table of Contents: Theory of Learning and Generalization Two Approaches to the Learning Problem Estimation of the Probability Measure and Problem of Learning. Conditions for Consistency of Empirical Risk Minimization Principle. The Structural Risk Minimization Principle. Stochastic Ill-Posed Problems. Support Vector Estimation of Functions. Perceptron's and Their Generalizations. SV Machines for Function Approximations, Regression Estimation and Signal Processing. Statistical Foundation of Learning Theory Necessary and Sufficient Conditions for Uniform Convergence of Frequencies to Their Probabilities Necessary and Sufficient Conditions for Uniform One-Sided Convergence of Means to Their Expectations Comments and Bibliographical Remarks References Index.
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It is particularly germane to this series which is concerned with "Adaptive and Learning Systems", specifically as applied in neural networks, fuzzy systems and artificial intelligence. Table of Contents: Theory of Learning and Generalization Two Approaches to the Learning Problem Estimation of the Probability Measure and Problem of Learning. Conditions for Consistency of Empirical Risk Minimization Principle. The Structural Risk Minimization Principle. Stochastic Ill-Posed Problems. Support Vector Estimation of Functions. Perceptron's and Their Generalizations. SV Machines for Function Approximations, Regression Estimation and Signal Processing. Statistical Foundation of Learning Theory Necessary and Sufficient Conditions for Uniform Convergence of Frequencies to Their Probabilities Necessary and Sufficient Conditions for Uniform One-Sided Convergence of Means to Their Expectations Comments and Bibliographical Remarks References Index.
Vladimir N. Vapnik is the author of Statistical Learning Theory.
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| Title | Statistical Learning Theory |
| Author | Vladimir N. Vapnik |
| ISBN-13 | 9788126528929 |
| Publication | STAR EXCLUSIVE |
It is particularly germane to this series which is concerned with "Adaptive and Learning Systems", specifically as applied in neural networks, fuzzy systems and artificial intelligence. Table of Contents: Theory of Learning and Generalization Two Approaches to the Learning Problem Estimation of the Probability Measure and Problem of Learning. Conditions for Consistency of Empirical Risk Minimization Principle. The Structural Risk Minimization Principle. Stochastic Ill-Posed Problems. Support Vector Estimation of Functions. Perceptron's and Their Generalizations. SV Machines for Function Approximations, Regression Estimation and Signal Processing. Statistical Foundation of Learning Theory Necessary and Sufficient Conditions for Uniform Convergence of Frequencies to Their Probabilities Necessary and Sufficient Conditions for Uniform One-Sided Convergence of Means to Their Expectations Comments and Bibliographical Remarks References Index.