Meta-learning in computational intelligence
Computational Intelligence (CI) community has developed hundreds of algorithms for intelligent data analysis, but still many hard problems in computer vision, signal processing or text and multimedia understanding, problems that require deep learning techniques, are open. Modern data mining packages...
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| Glavni autor: | |
|---|---|
| Daljnji autori: | , , , |
| Format: | Livre numérique |
| Jezik: | Anglais |
| Izdano: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Izdanje: | 1st ed. 2011. |
| Serija: | Studies in Computational Intelligence
358 |
| Online pristup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Bilješka: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Meta-Learning in Computational Intelligence, Norbert Jankowski, Włodzisław Duch and Krzysztof Gr—abczewski (Eds), 2011, Berlin, Springer, Studies in Computational Intelligence, 978-3-642-20979-6 • Meta-Learning in Computational Intelligence, Texte imprimé, 9783642268588 • Meta-Learning in Computational Intelligence, Norbert Jankowski, Włodzisław Duch and Krzysztof Gr—abczewski (Eds), 2011, Berlin, Springer, Studies in Computational Intelligence, 978-3-642-20979-6 • Meta-Learning in Computational Intelligence, Texte imprimé, 9783642209819 |
Sadržaj:
- Universal meta-learning architecture and algorithms Meta-learning of instance selection for data summarization Choosing the metric: a simple model approach Meta-learning Architectures: Collecting, Organizing and Exploiting Meta-knowledge Computational intelligence for meta-learning: a promising avenue of research Self-organization of supervised models Selecting Machine Learning Algorithms Using the Ranking Meta-Learning Approach A Meta-Model Perspective and Attribute Grammar Approach to Facilitating the Development of Novel Neural Network Models Ontology-Based Meta-Mining of Knowledge Discovery Workflows Optimal Support Features for Meta-learning