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...
| Other Authors: | , , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2009, 2009
|
| Edition: | 1st ed. 2009 |
| Series: | Studies in Computational Intelligence
|
| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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