| Titre : |
Learning machines : foundations of trainable pattern-classifying systems |
| Type de document : |
texte imprimé |
| Auteurs : |
Nils J. Nilsson, Auteur |
| Editeur : |
New York : McGraw-Hill |
| Année de publication : |
1965 |
| Collection : |
McGraw-Hill series in systems science |
| Importance : |
XI,137 p. |
| Présentation : |
ill. |
| Format : |
23 cm |
| Note générale : |
Bibliogr. at the end of chapters. - Index |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Intelligence artificielle
Artificial intelligence |
| Index. décimale : |
004.8 Intelligence artificielle |
| Résumé : |
This monograph presents some of the results of research in the new and exciting field of learning machines. A learning machine, broadly defined, is any device whose actions are influenced by past experiences. The present work deals specifically with the theory of a subclass of learning machines, those which can be trained to recognize patterns. Some well-known examples of trainable pattern-classifying systems are the perceptron and the madaline and minos networks. |
| Note de contenu : |
Summary :
1. Trainable pattern classifiers.
2. Some important discriminant functions : their properties and their implementations.
3. Parametric training methods.
4. Some nonparametric training methods for machines.
5. Training theorems.
6. Layered machines.
7. Piecewise linear machines. |
Learning machines : foundations of trainable pattern-classifying systems [texte imprimé] / Nils J. Nilsson, Auteur . - New York : McGraw-Hill, 1965 . - XI,137 p. : ill. ; 23 cm. - ( McGraw-Hill series in systems science) . Bibliogr. at the end of chapters. - Index Langues : Anglais ( eng)
| Mots-clés : |
Intelligence artificielle
Artificial intelligence |
| Index. décimale : |
004.8 Intelligence artificielle |
| Résumé : |
This monograph presents some of the results of research in the new and exciting field of learning machines. A learning machine, broadly defined, is any device whose actions are influenced by past experiences. The present work deals specifically with the theory of a subclass of learning machines, those which can be trained to recognize patterns. Some well-known examples of trainable pattern-classifying systems are the perceptron and the madaline and minos networks. |
| Note de contenu : |
Summary :
1. Trainable pattern classifiers.
2. Some important discriminant functions : their properties and their implementations.
3. Parametric training methods.
4. Some nonparametric training methods for machines.
5. Training theorems.
6. Layered machines.
7. Piecewise linear machines. |
|
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