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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2009.
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| Edición: | 1st ed. 2009. |
| Colección: | Studies in Computational Intelligence,
201 |
| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- 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
- 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 Feedforward Neural Networks
- Workload Assignment in Production Networks by Multi Agent Architecture
- 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
- 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.