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ICANN
2005
Springer
15 years 3 months ago
Training of Support Vector Machines with Mahalanobis Kernels
Abstract. Radial basis function (RBF) kernels are widely used for support vector machines. But for model selection, we need to optimize the kernel parameter and the margin paramete...
Shigeo Abe
PVM
2005
Springer
15 years 3 months ago
Some Improvements to a Parallel Decomposition Technique for Training Support Vector Machines
We consider a parallel decomposition technique for solving the large quadratic programs arising in training the learning methodology Support Vector Machine. At each iteration of th...
Thomas Serafini, Luca Zanni, Gaetano Zanghirati
ICDM
2009
IEEE
160views Data Mining» more  ICDM 2009»
15 years 4 months ago
Fast Online Training of Ramp Loss Support Vector Machines
—A fast online algorithm OnlineSVMR for training Ramp-Loss Support Vector Machines (SVMR s) is proposed. It finds the optimal SVMR for t+1 training examples using SVMR built on t...
Zhuang Wang, Slobodan Vucetic
ICPR
2008
IEEE
15 years 4 months ago
Fast model selection for MaxMinOver-based training of support vector machines
OneClassMaxMinOver (OMMO) is a simple incremental algorithm for one-class support vector classification. We propose several enhancements and heuristics for improving model select...
Fabian Timm, Sascha Klement, Thomas Martinetz
ACL
2011
14 years 1 months ago
Joint Training of Dependency Parsing Filters through Latent Support Vector Machines
Graph-based dependency parsing can be sped up significantly if implausible arcs are eliminated from the search-space before parsing begins. State-of-the-art methods for arc filt...
Colin Cherry, Shane Bergsma