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» Methods for convex and general quadratic programming
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KSEM
2009
Springer
16 years 15 days 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 7 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»
16 years 12 days 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 5 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 10 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