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...
Shranjeno v:
| Glavni avtor: | |
|---|---|
| Drugi avtorji: | , |
| Format: | Livre numérique |
| Jezik: | Anglais |
| Izdano: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Serija: | Lecture notes in computer science
661 |
| Teme: | |
| Online dostop: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Sporočilo: |
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 |
Kazalo:
- Strategic directions in machine learning
- Training a 3-node neural network is NP-complete
- Cryptographic limitations on learning Boolean formulae and finite automata
- Inference of finite automata using homing sequences
- Adaptive search by learning from incomplete explanations of failures
- Learning of rules for fault diagnosis in power supply networks
- Cross references are features
- The schema mechanism
- L-ATMS: A tight integration of EBL and the ATMS
- Massively parallel symbolic induction of protein structure/function relationships
- Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks
- Phoneme discrimination using connectionist networks
- Behavior-based learning to control IR oven heating: Preliminary investigations
- Trellis codes, receptive fields, and fault tolerant, self-repairing neural networks.