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- catalog contributor b11969345.
- catalog contributor b11969346.
- catalog created "1999.".
- catalog date "1999".
- catalog date "1999.".
- catalog dateCopyrighted "1999.".
- catalog description "1. Introduction -- Pt. 1. Pattern Classification with Binary-Output Neural Networks -- 2. The Pattern Classification Problem -- 3. The Growth Function and VC-Dimension -- 4. General Upper Bounds on Sample Complexity -- 5. General Lower Bounds on Sample Complexity -- 6. The VC-Dimension of Linear Threshold Networks -- 7. Bounding the VC-Dimension using Geometric Techniques -- 8. Vapnik-Chervonenkis Dimension Bounds for Neural Networks -- Pt. 2. Pattern Classification with Real-Output Networks -- 9. Classification with Real-Valued Functions -- 10. Covering Numbers and Uniform Convergence -- 11. The Pseudo-Dimension and Fat-Shattering Dimension -- 12. Bounding Covering Numbers with Dimensions -- 13. The Sample Complexity of Classification Learning -- 14. The Dimensions of Neural Networks -- 15. Model Selection -- Pt. 3. Learning Real-Valued Functions -- 16. Learning Classes of Real Functions -- 17. Uniform Convergence Results for Real Function Classes -- 18. Bounding Covering Numbers -- 19. Sample Complexity of Learning Real Function Classes -- 20. Convex Classes -- 21. Other Learning Problems -- Pt. 4. Algorithmics -- 22. Efficient Learning -- 23. Learning as Optimization -- 24. The Boolean Perceptron -- 25. Hardness Results for Feed-Forward Networks -- 26. Constructive Learning Algorithms for Two-Layer Networks.".
- catalog description "Includes bibliographical references (p. 365-378) and indexes.".
- catalog extent "xiv, 389 p. :".
- catalog identifier "052157353X (hardback)".
- catalog issued "1999".
- catalog issued "1999.".
- catalog language "eng".
- catalog publisher "Cambridge ; New York, NY : Cambridge University Press,".
- catalog subject "006.3/2 21".
- catalog subject "Neural networks (Computer science)".
- catalog subject "QA76.87 .A58 1999".
- catalog tableOfContents "1. Introduction -- Pt. 1. Pattern Classification with Binary-Output Neural Networks -- 2. The Pattern Classification Problem -- 3. The Growth Function and VC-Dimension -- 4. General Upper Bounds on Sample Complexity -- 5. General Lower Bounds on Sample Complexity -- 6. The VC-Dimension of Linear Threshold Networks -- 7. Bounding the VC-Dimension using Geometric Techniques -- 8. Vapnik-Chervonenkis Dimension Bounds for Neural Networks -- Pt. 2. Pattern Classification with Real-Output Networks -- 9. Classification with Real-Valued Functions -- 10. Covering Numbers and Uniform Convergence -- 11. The Pseudo-Dimension and Fat-Shattering Dimension -- 12. Bounding Covering Numbers with Dimensions -- 13. The Sample Complexity of Classification Learning -- 14. The Dimensions of Neural Networks -- 15. Model Selection -- Pt. 3. Learning Real-Valued Functions -- 16. Learning Classes of Real Functions -- 17. Uniform Convergence Results for Real Function Classes -- 18. Bounding Covering Numbers -- 19. Sample Complexity of Learning Real Function Classes -- 20. Convex Classes -- 21. Other Learning Problems -- Pt. 4. Algorithmics -- 22. Efficient Learning -- 23. Learning as Optimization -- 24. The Boolean Perceptron -- 25. Hardness Results for Feed-Forward Networks -- 26. Constructive Learning Algorithms for Two-Layer Networks.".
- catalog title "Neural network learning : theoretical foundations / Martin Anthony and Peter L. Bartlett.".
- catalog type "text".