Machine learning : from theory to applications : cooperative research at Siemens and MIT
This volume includes some of the key research papers in the area of machine learning produced at MIT and Siemens during a three-year joint research effort. It includes papers on many different styles of machine learning, organized into three parts. Part I, theory, includes three papers on theoretica...
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| Další autoři: | , |
| Médium: | Livre numérique |
| Jazyk: | Anglais |
| Vydáno: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Edice: | Lecture notes in computer science
661 |
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| On-line přístup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Machine learning, from theory to applications, cooperative research at Siemens and MIT, S.J. Hanson, W. Remmele, R.L. Rivest, eds, Berlin, Springer, 1993, 1 vol. (VIII-271 p.), Lecture notes in computer science, 0-387-56483-7 • Machine Learning: From Theory to Applications, Texte imprimé, 9783662164860 |
| Shrnutí: | This volume includes some of the key research papers in the area of machine learning produced at MIT and Siemens during a three-year joint research effort. It includes papers on many different styles of machine learning, organized into three parts. Part I, theory, includes three papers on theoretical aspects of machine learning. The first two use the theory of computational complexity to derive some fundamental limits on what isefficiently learnable. The third provides an efficient algorithm for identifying finite automata. Part II, artificial intelligence and symbolic learning methods, includes five papers giving an overview of the state of the art and future developments in the field of machine learning, a subfield of artificial intelligence dealing with automated knowledge acquisition and knowledge revision. Part III, neural and collective computation, includes five papers sampling the theoretical diversity and trends in the vigorous new research field of neural networks: massively parallel symbolic induction, task decomposition through competition, phoneme discrimination, behavior-based learning, and self-repairing neural networks. |
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| Popis jednotky: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9783540475682 (PDF) |
| ISSN: | 1611-3349 |
| Přístup: | Accès en ligne pour les établissements français bénéficiaires des licences nationales Accès soumis à abonnement pour tout autre établissement Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |