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» Methods for convex and general quadratic programming
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KSEM
2009
Springer
15 years 11 months ago
A Competitive Learning Approach to Instance Selection for Support Vector Machines
Abstract. Support Vector Machines (SVM) have been applied successfully in a wide variety of fields in the last decade. The SVM problem is formulated as a convex objective function...
Mario Zechner, Michael Granitzer
ICML
2010
IEEE
15 years 5 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
ICDM
2008
IEEE
160views Data Mining» more  ICDM 2008»
15 years 11 months ago
Direct Zero-Norm Optimization for Feature Selection
Zero-norm, defined as the number of non-zero elements in a vector, is an ideal quantity for feature selection. However, minimization of zero-norm is generally regarded as a combi...
Kaizhu Huang, Irwin King, Michael R. Lyu
IJRR
2002
129views more  IJRR 2002»
15 years 4 months ago
Deformable Free-Space Tilings for Kinetic Collision Detection
We present kinetic data structures for detecting collisions between a set of polygons that are moving continuously. Unlike classical collision detection methods that rely on bound...
Pankaj K. Agarwal, Julien Basch, Leonidas J. Guiba...
CVPR
1997
IEEE
15 years 8 months ago
Autocalibration and the absolute quadric
We describe a new method for camera autocalibration and scaled Euclidean structure and motion, from three or more views taken by a moving camera with fixed but unknown intrinsic ...
Bill Triggs