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» Kernel Machines and Boolean Functions
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ESANN
2007
13 years 9 months ago
Optimizing kernel parameters by second-order methods
Radial basis function network (RBF) kernels are widely used for support vector machines (SVMs). But for model selection of an SVM, we need to optimize the kernel parameter and the ...
Shigeo Abe
PPSN
2010
Springer
13 years 5 months ago
Comparison-Based Optimizers Need Comparison-Based Surrogates
Abstract. Taking inspiration from approximate ranking, this paper investigates the use of rank-based Support Vector Machine as surrogate model within CMA-ES, enforcing the invarian...
Ilya Loshchilov, Marc Schoenauer, Michèle S...
ICML
2001
IEEE
14 years 8 months ago
Learning with the Set Covering Machine
We generalize the classical algorithms of Valiant and Haussler for learning conjunctions and disjunctions of Boolean attributes to the problem of learning these functions over arb...
Mario Marchand, John Shawe-Taylor
ICANN
2001
Springer
13 years 12 months ago
Incremental Support Vector Machine Learning: A Local Approach
Abstract. In this paper, we propose and study a new on-line algorithm for learning a SVM based on Radial Basis Function Kernel: Local Incremental Learning of SVM or LISVM. Our meth...
Liva Ralaivola, Florence d'Alché-Buc
SNPD
2004
13 years 8 months ago
Using extended phylogenetc profiles and support vector machines for protein family classification
We proposed a new approach to compare profiles when the correlations among attributes can be represented as a tree. To account for these correlations, the profile is extended with...
Kishore Narra, Li Liao