Neural network learning / Martin (1941-....) Anthony (1999)

Public ISBD Titre : Neural network learning : theoretical foundations Type de document : texte imprimé Auteurs : Martin (1941-....) Anthony, Auteur ; Bartlett, Peter L., Auteur Editeur : Cambridge Année de publication : 1999 Importance : XIV, 389 p Présentation : ill. Format : 23 cm ISBN/ISSN/EAN : 978-0-521-11862-0 Langues : Anglais (eng) Mots-clés : Neural networks (Computer science) Ordinateurs neuronaux Algorithmes Réseaux neuronaux (informatique) Index. décimale : 681.3.022 Périphérique.Connecté(on-line).Terminaux. Résumé : This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Research on pattern classification with binary-output networks is surveyed, including a discussion of the relevance of the Vapnik-Chervonenkis dimension, and calculating estimates of the dimension for several neural network models. A model of classification by real-output networks is developed, and the usefulness of classification with a 'large margin' is demonstrated. The authors explain the role of scale-sensitive versions of the Vapnik-Chervonenkis dimension in large margin classification, and in real prediction. They also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient constructive learning algorithms. The book is self-contained and is intended to be accessible to researchers and graduate students in computer science, engineering, and mathematics. Note de contenu : Summary : Part I. Pattern Recognition with Binary-output Neural Networks 2. The pattern recognition problem 3. The growth function and VC-dimension 4. General upper bounds on sample complexity 5. General lower bounds 6. The VC-dimension of linear threshold networks 7. Bounding the VC-dimension using geometric techniques 8. VC-dimension bounds for neural networks Part II. Pattern Recognition with Real-output Neural Networks 9. Classification with real values 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 Part III. Learning Real-Valued Functions 16. Learning classes of real functions 17. Uniform convergence results for real function classes 18. Bounding covering numbers 19. The sample complexity of learning function classes 20. Convex classes 21. Other learning problems Part IV. Algorithmics 22. Efficient learning 23. Learning as optimisation 24. The Boolean perceptron 25. Hardness results for feed-forward networks 26. Constructive learning algorithms for two-layered networks Neural network learning : theoretical foundations [texte imprimé] / Martin (1941-....) Anthony, Auteur ; Bartlett, Peter L., Auteur . - Cambridge, 1999 . - XIV, 389 p : ill. ; 23 cm. ISBN : 978-0-521-11862-0 Langues : Anglais (eng) Mots-clés : Neural networks (Computer science) Ordinateurs neuronaux Algorithmes Réseaux neuronaux (informatique) Index. décimale : 681.3.022 Périphérique.Connecté(on-line).Terminaux. Résumé : This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Research on pattern classification with binary-output networks is surveyed, including a discussion of the relevance of the Vapnik-Chervonenkis dimension, and calculating estimates of the dimension for several neural network models. A model of classification by real-output networks is developed, and the usefulness of classification with a 'large margin' is demonstrated. The authors explain the role of scale-sensitive versions of the Vapnik-Chervonenkis dimension in large margin classification, and in real prediction. They also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient constructive learning algorithms. The book is self-contained and is intended to be accessible to researchers and graduate students in computer science, engineering, and mathematics. Note de contenu : Summary : Part I. Pattern Recognition with Binary-output Neural Networks 2. The pattern recognition problem 3. The growth function and VC-dimension 4. General upper bounds on sample complexity 5. General lower bounds 6. The VC-dimension of linear threshold networks 7. Bounding the VC-dimension using geometric techniques 8. VC-dimension bounds for neural networks Part II. Pattern Recognition with Real-output Neural Networks 9. Classification with real values 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 Part III. Learning Real-Valued Functions 16. Learning classes of real functions 17. Uniform convergence results for real function classes 18. Bounding covering numbers 19. The sample complexity of learning function classes 20. Convex classes 21. Other learning problems Part IV. Algorithmics 22. Efficient learning 23. Learning as optimisation 24. The Boolean perceptron 25. Hardness results for feed-forward networks 26. Constructive learning algorithms for two-layered networks

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Code-barres	Cote	Support	Localisation	Section	Disponibilité	Etat_Exemplaire
052166	681.3.022 ANT	Papier	Bibliothèque Centrale	Informatique	Disponible	Consultation sur place