Kernel-based Data Fusion for Machine Learning : Methods and Applications in Bioinformatics and Text Mining
Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then...
Gardado en:
| Auteurs principaux: | , , , |
|---|---|
| Formato: | Livre numérique |
| Idioma: | Anglais |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Edición: | 1st ed. 2011. |
| Series: | Studies in Computational Intelligence
345 |
| Acceso en liña: | 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: | • Kernel-based Data Fusion for Machine Learning, Texte imprimé, 9783642194054 • Kernel-based Data Fusion for Machine Learning, Texte imprimé, 9783642267512 • Kernel-based Data Fusion for Machine Learning, Texte imprimé, 9783642194054 • Kernel-based Data Fusion for Machine Learning, Texte imprimé, 9783642194078 |
Table des matières:
- Introduction Rayleigh quotient-type problems in machine learning Ln-norm Multiple Kernel Learning and Least Squares Support VectorMachines Optimized data fusion for kernel k-means Clustering Multi-view text mining for disease gene prioritization and clustering Optimized data fusion for k-means Laplacian Clustering Weighted Multiple Kernel Canonical Correlation Cross-species candidate gene prioritization with MerKator Conclusion