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ICML
2010
IEEE
13 years 9 months ago
Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets
A sparse representation of Support Vector Machines (SVMs) with respect to input features is desirable for many applications. In this paper, by introducing a 0-1 control variable t...
Mingkui Tan, Li Wang, Ivor W. Tsang
ICASSP
2011
IEEE
12 years 11 months ago
Subspace pursuit method for kernel-log-linear models
This paper presents a novel method for reducing the dimensionality of kernel spaces. Recently, to maintain the convexity of training, loglinear models without mixtures have been u...
Yotaro Kubo, Simon Wiesler, Ralf Schlüter, He...
ICML
2005
IEEE
14 years 8 months ago
A support vector method for multivariate performance measures
This paper presents a Support Vector Method for optimizing multivariate nonlinear performance measures like the F1score. Taking a multivariate prediction approach, we give an algo...
Thorsten Joachims
CAGD
2002
102views more  CAGD 2002»
13 years 7 months ago
Optimal slicing of free-form surfaces
Many applications, such as contour machining, rapid prototyping, and reverse engineering by laser scanner or coordinate measuring machine, involve sampling of free-from surfaces a...
Tait S. Smith, Rida T. Farouki, Mohammad al-Kandar...
ICIC
2005
Springer
14 years 1 months ago
Methods of Decreasing the Number of Support Vectors via k-Mean Clustering
This paper proposes two methods which take advantage of k -mean clustering algorithm to decrease the number of support vectors (SVs) for the training of support vector machine (SVM...
Xiao-Lei Xia, Michael R. Lyu, Tat-Ming Lok, Guang-...