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» Tangent Distance Kernels for Support Vector Machines
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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
NIPS
2007
13 years 9 months ago
Learning with Transformation Invariant Kernels
This paper considers kernels invariant to translation, rotation and dilation. We show that no non-trivial positive definite (p.d.) kernels exist which are radial and dilation inv...
Christian Walder, Olivier Chapelle
SDM
2009
SIAM
119views Data Mining» more  SDM 2009»
14 years 4 months ago
Twin Vector Machines for Online Learning on a Budget.
This paper proposes Twin Vector Machine (TVM), a constant space and sublinear time Support Vector Machine (SVM) algorithm for online learning. TVM achieves its favorable scaling b...
Zhuang Wang, Slobodan Vucetic
NECO
2010
101views more  NECO 2010»
13 years 2 months ago
Large-Margin Classification in Infinite Neural Networks
We introduce a new family of positive-definite kernels for large margin classification in support vector machines (SVMs). These kernels mimic the computation in large neural netwo...
Youngmin Cho, Lawrence K. Saul
ICML
2005
IEEE
14 years 8 months ago
Building Sparse Large Margin Classifiers
This paper presents an approach to build Sparse Large Margin Classifiers (SLMC) by adding one more constraint to the standard Support Vector Machine (SVM) training problem. The ad...
Bernhard Schölkopf, Gökhan H. Bakir, Min...