international law / Shaw Malcolm N. (1997)

Public ISBD Titre : international law Type de document : texte imprimé Auteurs : Shaw Malcolm N., Auteur Mention d'édition : 4e éd Editeur : Cambridge Année de publication : 1997 Importance : xivi-939 p. Présentation : ill. Format : 24 cm ISBN/ISSN/EAN : 978-0-521-62671-2 Note générale : index Langues : Anglais (eng) Mots-clés : droit loi internationale et municipal droits de l'homme état Index. décimale : 341 Droit international Résumé : Cambridge university press is part of the university of cambridge.It is a charitable enterprise dedicated to printing and publishing for the advancement of knowledge throughout the world.In recognition of the fact that there are many parts of the world where people have difficulty in purchasing their own copies of the best international textbooks, cambridge university press is now publishing a series of low priced editions.These are reprints of internationally respected books from cambridge university press, specially printed and priced for the benefit of students and teachers in selected countries. Note de contenu : 1.The nature and development of international law 2.International law today 3.Sources 4.International law and municipal law 5.The subjects of international law 6.The international protection of human rights 7.The regional protection of human rights 8.Recognition ... international law [texte imprimé] / Shaw Malcolm N., Auteur  . -  4e éd . - Cambridge, 1997 . - xivi-939 p. : ill. ; 24 cm. ISBN : 978-0-521-62671-2 index Langues : Anglais (eng) Mots-clés : droit loi internationale et municipal droits de l'homme état Index. décimale : 341 Droit international Résumé : Cambridge university press is part of the university of cambridge.It is a charitable enterprise dedicated to printing and publishing for the advancement of knowledge throughout the world.In recognition of the fact that there are many parts of the world where people have difficulty in purchasing their own copies of the best international textbooks, cambridge university press is now publishing a series of low priced editions.These are reprints of internationally respected books from cambridge university press, specially printed and priced for the benefit of students and teachers in selected countries. Note de contenu : 1.The nature and development of international law 2.International law today 3.Sources 4.International law and municipal law 5.The subjects of international law 6.The international protection of human rights 7.The regional protection of human rights 8.Recognition ...

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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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Statistics / Harry Frank (1994)

Public ISBD Titre : Statistics : concepts and applications Type de document : texte imprimé Auteurs : Harry Frank, Auteur ; Steven C Althoen, Auteur Editeur : Cambridge Année de publication : 1994 Importance : xxvi-853 p. Présentation : ill. Format : 25 cm ISBN/ISSN/EAN : 978-0-521-49852-4 Note générale : Index Mots-clés : statistiques Index. décimale : 519.22 Théorie statistique. Modèles statistiques. Statistiques mathématiques Résumé : This is a 'classical' general statistics text written in a modern voice.the authors bring mathematical,theoretical and conceptual integrity to a body of topics and techniques that is appropriate to a first course in statistics and do so in a way that is accessible to students whose mathematical preparation does not go beyond the standard curriculum for college algebra.The informal approach provides conceptual richness and develops a subtext of mathematical instruction that achieves a high level of rigour.The text presents a thorough,step-by-step development of fundamental principles. Note de contenu : Part 1.Organization and description of data Part 2.Probability Part 3.Introduction to statistical inference Part 4.Hypothesis testing:intermediate techniques Statistics : concepts and applications [texte imprimé] / Harry Frank, Auteur ; Steven C Althoen, Auteur . - Cambridge, 1994 . - xxvi-853 p. : ill. ; 25 cm. ISBN : 978-0-521-49852-4 Index Mots-clés : statistiques Index. décimale : 519.22 Théorie statistique. Modèles statistiques. Statistiques mathématiques Résumé : This is a 'classical' general statistics text written in a modern voice.the authors bring mathematical,theoretical and conceptual integrity to a body of topics and techniques that is appropriate to a first course in statistics and do so in a way that is accessible to students whose mathematical preparation does not go beyond the standard curriculum for college algebra.The informal approach provides conceptual richness and develops a subtext of mathematical instruction that achieves a high level of rigour.The text presents a thorough,step-by-step development of fundamental principles. Note de contenu : Part 1.Organization and description of data Part 2.Probability Part 3.Introduction to statistical inference Part 4.Hypothesis testing:intermediate techniques

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