Inferring Sparse Kernel Combinations and Relevance Vectors: An Application to Subcellular Localization of Proteins

In this paper, we introduce two new formulations for multi-class multi-kernel relevance vector machines (m-RVMs) that explicitly lead to sparse solutions, both in samples and in number of kernels. This enables their application to large-scale multi-feature multinomial classification problems where t...

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Bibliographische Detailangaben

Hauptverfasser:	Damoulas, T., Ying, Y., Girolami, M.A., Campbell, C.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Bayesian methods Bioinformatics Extraterrestrial measurements Kernel Kernel combination Large-scale systems Machine learning Mathematics Optimization methods Protein engineering Protein subcellular localization Relevance vector machine Sparsity Support vector machines
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