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...
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| Altri autori: | , , , , |
|---|---|
| Natura: | Livre numérique |
| Lingua: | Anglais |
| Pubblicazione: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Edizione: | 1st ed. 2009. |
| Serie: | Studies in Computational Intelligence
201 |
| Accesso online: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Foundations of computational intelligence, Volume 1, Learning and approximation, Aboul-Ella Hassanien, Ajith Abraham, Athanasios V. Vasilakos and Witold Pedrycz (eds), Berlin Heidelberg, Springer, 2009, 1 vol. (XII-397 p.), Studies in computational intelligence, 978-3-642-01081-1 • Foundations of Computational Intelligence, Texte imprimé, 9783642010835 • Foundations of computational intelligence, Volume 1, Learning and approximation, Aboul-Ella Hassanien, Ajith Abraham, Athanasios V. Vasilakos and Witold Pedrycz (eds), Berlin Heidelberg, Springer, 2009, 1 vol. (XII-397 p.), Studies in computational intelligence, 978-3-642-01081-1 • Foundations of Computational Intelligence, Texte imprimé, 9783662568439 |
Sommario:
- 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