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CORR
2006
Springer
130views Education» more  CORR 2006»
14 years 9 months ago
Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection
Abstract. Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed opti...
Christian Gagné, Marc Schoenauer, Mich&egra...
ALT
2002
Springer
15 years 6 months ago
On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum
Abstract. In this paper we analyze the relationships between the eigenvalues of the m × m Gram matrix K for a kernel k(·, ·) corresponding to a sample x1, . . . , xm drawn from ...
John Shawe-Taylor, Christopher K. I. Williams, Nel...
ML
2002
ACM
223views Machine Learning» more  ML 2002»
14 years 9 months ago
Text Categorization with Support Vector Machines. How to Represent Texts in Input Space?
The choice of the kernel function is crucial to most applications of support vector machines. In this paper, however, we show that in the case of text classification, term-frequenc...
Edda Leopold, Jörg Kindermann
ICIP
2005
IEEE
15 years 11 months ago
Semantic kernel learning for interactive image retrieval
Content-based image retrieval systems still have difficulties to bridge the semantic gap between the low-level representation of images and the high level concepts the user is loo...
Philippe Henri Gosselin, Matthieu Cord
ICML
2009
IEEE
15 years 10 months ago
Learning kernels from indefinite similarities
Similarity measures in many real applications generate indefinite similarity matrices. In this paper, we consider the problem of classification based on such indefinite similariti...
Yihua Chen, Maya R. Gupta, Benjamin Recht