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» Training of Support Vector Machines with Mahalanobis Kernels
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ESANN
2004
15 years 7 months ago
Sparse LS-SVMs using additive regularization with a penalized validation criterion
This paper is based on a new way for determining the regularization trade-off in least squares support vector machines (LS-SVMs) via a mechanism of additive regularization which ha...
Kristiaan Pelckmans, Johan A. K. Suykens, Bart De ...
ICPR
2010
IEEE
15 years 9 months ago
Localized Multiple Kernel Regression
Multiple kernel learning (MKL) uses a weighted combination of kernels where the weight of each kernel is optimized during training. However, MKL assigns the same weight to a kerne...
Mehmet Gönen, Ethem Alpaydin
JMLR
2006
89views more  JMLR 2006»
15 years 6 months ago
Maximum-Gain Working Set Selection for SVMs
Support vector machines are trained by solving constrained quadratic optimization problems. This is usually done with an iterative decomposition algorithm operating on a small wor...
Tobias Glasmachers, Christian Igel
ILP
2007
Springer
16 years 8 days ago
A Phase Transition-Based Perspective on Multiple Instance Kernels
: This paper is concerned with relational Support Vector Machines, at the intersection of Support Vector Machines (SVM) and relational learning or Inductive Logic Programming (ILP)...
Romaric Gaudel, Michèle Sebag, Antoine Corn...
ICFCA
2009
Springer
15 years 3 months ago
A Concept Lattice-Based Kernel for SVM Text Classification
Abstract. Standard Support Vector Machines (SVM) text classification relies on bag-of-words kernel to express the similarity between documents. We show that a document lattice can ...
Claudio Carpineto, Carla Michini, Raffaele Nicolus...