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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2011.
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| Edición: | 1st ed. 2011. |
| Colección: | Studies in Computational Intelligence,
345 |
| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- 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.