Foundations of Computational, Intelligence Volume 1 Learning and Approximation /
Learning methods and approximation algorithms are fundamental tools that deal with computationally hard problems and problems in which the input is gradually disclosed over time. Both kinds of problems have a large number of applications arising from a variety of fields, such as algorithmic game the...
| Corporate Author: | |
|---|---|
| Other Authors: | , , , |
| Format: | Electronic |
| Language: | English |
| Published: |
Berlin, Heidelberg :
Springer Berlin Heidelberg,
2009.
|
| Series: | Studies in Computational Intelligence,
201 |
| Subjects: | |
| Online Access: | https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-01082-8 |
Table of Contents:
- Part I Function Approximation
- Machine Learning and Genetic Regulatory Networks: A Review and a Roadmap
- Automatic Approximation of Expensive Functions with Active Learning
- New Multi-Objective Algorithms for Neural Network Training applied to Genomic Classification Data
- An Evolutionary Approximation for the Coefficients of Decision Functions within a Support Vector Machine Learning Strategy
- Part II Connectionist Learning
- Meta-learning and Neurocomputing A New Perspective for Computational Intelligence
- Three-term Fuzzy Back-propagation
- Entropy Guided Transformation Learning
- Artificial Development
- Robust Training of Artificial Feed-forward Neural Networks
- Workload Assignment In Production Networks By Multi-Agent Architecture
- Part III Knowledge Representation and Acquisition
- Extensions to Knowledge Acquisition and Effect of Multimodal Representation in Unsupervised Learning
- A New Implementation for Neural Networks in Fourier-Space
- Part IV Learning and Visualization
- Dissimilarity Analysis and Application to Visual Comparisons
- Dynamic Self-Organising Maps: Theory, Methods and Applications
- Hybrid Learning Enhancement of RBF Network with Particle Swarm Optimization.