Innovations in neural information paradigms and applications
This research book presents some of the most recent advances in neural information processing models including both theoretical concepts and practical applications. The contributions include: Advances in neural information processing paradigms Self organising structures Unsupervised and supervised l...
Enregistré dans:
| Andre forfattere: | , , , |
|---|---|
| Format: | Livre numérique |
| Sprog: | Anglais |
| Udgivet: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Udgivelse: | 1st ed. 2009. |
| Serier: | Studies in Computational Intelligence
247 |
| Online adgang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Kommentar: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Innovations in Neural Information Paradigms and Applications, Texte imprimé, 9783642040023 • Innovations in Neural Information Paradigms and Applications, Texte imprimé, 9783642040368 • Innovations in Neural Information Paradigms and Applications, Texte imprimé, 9783642040023 • Innovations in Neural Information Paradigms and Applications, Texte imprimé, 9783642260971 |
Indholdsfortegnelse:
- Advances in Neural Information Processing Paradigms Self-Organizing Maps for Structured Domains: Theory, Models, and Learning of Kernels Unsupervised and Supervised Learning of Graph Domains Neural Grammar Networks Estimates of Model Complexity in Neural-Network Learning Regularization and Suboptimal Solutions in Learning from Data Probabilistic Interpretation of Neural Networks for the Classification of Vectors, Sequences and Graphs Metric Learning for Prototype-Based Classification Bayesian Linear Combination of Neural Networks Credit Card Transactions, Fraud Detection, and Machine Learning: Modelling Time with LSTM Recurrent Neural Networks Towards Computational Modelling of Neural Multimodal Integration Based on the Superior Colliculus Concept