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» Fast Support Vector Machine Classification using linear SVMs
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EMNLP
2009
14 years 11 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
129
Voted
CORR
2006
Springer
130views Education» more  CORR 2006»
15 years 1 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...
138
Voted
BMCBI
2007
153views more  BMCBI 2007»
15 years 1 months ago
Analysis of nanopore detector measurements using Machine-Learning methods, with application to single-molecule kinetic analysis
Background: A nanopore detector has a nanometer-scale trans-membrane channel across which a potential difference is established, resulting in an ionic current through the channel ...
Matthew Landry, Stephen Winters-Hilt
BMCBI
2007
154views more  BMCBI 2007»
15 years 2 months ago
Classification of heterogeneous microarray data by maximum entropy kernel
Background: There is a large amount of microarray data accumulating in public databases, providing various data waiting to be analyzed jointly. Powerful kernel-based methods are c...
Wataru Fujibuchi, Tsuyoshi Kato
134
Voted
ICIP
2006
IEEE
16 years 3 months ago
Fusion of Geometrical and Texture Information for Facial Expression Recognition
A novel method based on geometrical and texture information is proposed for facial expression recognition from video sequences. The Discriminant Non-negative Matrix Factorization ...
Irene Kotsia, Nikos Nikolaidis, Ioannis Pitas