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» Tangent Distance Kernels for Support Vector Machines
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EMNLP
2009
13 years 5 months ago
Reverse Engineering of Tree Kernel Feature Spaces
We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernelbased machine, e.g. ...
Daniele Pighin, Alessandro Moschitti
ICMCS
2005
IEEE
284views Multimedia» more  ICMCS 2005»
14 years 1 months ago
Conditionally Positive Definite Kernels for SVM Based Image Recognition
Kernel based methods such as Support Vector Machine (SVM) have provided successful tools for solving many recognition problems. One of the reason of this success is the use of ker...
Sabri Boughorbel, Jean-Philippe Tarel, Nozha Bouje...
SCIA
2005
Springer
137views Image Analysis» more  SCIA 2005»
14 years 1 months ago
Invariance in Kernel Methods by Haar-Integration Kernels
Abstract. We address the problem of incorporating transformation invariance in kernels for pattern analysis with kernel methods. We introduce a new class of kernels by so called Ha...
Bernard Haasdonk, A. Vossen, Hans Burkhardt
ESANN
2007
13 years 9 months ago
Model Selection for Kernel Probit Regression
Abstract. The convex optimisation problem involved in fitting a kernel probit regression (KPR) model can be solved efficiently via an iteratively re-weighted least-squares (IRWLS)...
Gavin C. Cawley
ICIP
2009
IEEE
13 years 5 months ago
Efficient reduction of support vectors in kernel-based methods
Kernel-based methods, e.g., support vector machine (SVM), produce high classification performances. However, the computation becomes time-consuming as the number of the vectors su...
Takumi Kobayashi, Nobuyuki Otsu