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» Oblique Support Vector Machines
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ICML
2009
IEEE
14 years 9 months ago
Group lasso with overlap and graph lasso
We propose a new penalty function which, when used as regularization for empirical risk minimization procedures, leads to sparse estimators. The support of the sparse vector is ty...
Laurent Jacob, Guillaume Obozinski, Jean-Philippe ...
ADMA
2005
Springer
134views Data Mining» more  ADMA 2005»
13 years 11 months ago
An LZ78 Based String Kernel
We develop the notion of normalized information distance (NID) [7] into a kernel distance suitable for use with a Support Vector Machine classifier, and demonstrate its use for an...
Ming Li, Ronan Sleep
NAACL
2010
13 years 6 months ago
An extractive supervised two-stage method for sentence compression
We present a new method that compresses sentences by removing words. In a first stage, it generates candidate compressions by removing branches from the source sentence's dep...
Dimitrios Galanis, Ion Androutsopoulos
ICASSP
2010
IEEE
13 years 7 months ago
Bayesian compressive sensing for phonetic classification
In this paper, we introduce a novel bayesian compressive sensing (CS) technique for phonetic classification. CS is often used to characterize a signal from a few support training...
Tara N. Sainath, Avishy Carmi, Dimitri Kanevsky, B...
PKDD
2009
Springer
88views Data Mining» more  PKDD 2009»
14 years 3 months ago
Feature Weighting Using Margin and Radius Based Error Bound Optimization in SVMs
The Support Vector Machine error bound is a function of the margin and radius. Standard SVM algorithms maximize the margin within a given feature space, therefore the radius is fi...
Huyen Do, Alexandros Kalousis, Melanie Hilario