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ICML
2010
IEEE
13 years 12 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
JMLR
2006
107views more  JMLR 2006»
13 years 11 months ago
Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss
The consistency of classification algorithm plays a central role in statistical learning theory. A consistent algorithm guarantees us that taking more samples essentially suffices...
Di-Rong Chen, Tao Sun
ICCV
2011
IEEE
12 years 11 months ago
From Contours to 3D Object Detection and Pose Estimation
This paper addresses view-invariant object detection and pose estimation from a single image. While recent work focuses on object-centered representations of point-based object fe...
Nadia Payet, Sinisa Todorovic
PR
2010
163views more  PR 2010»
13 years 9 months ago
Optimal feature selection for support vector machines
Selecting relevant features for Support Vector Machine (SVM) classifiers is important for a variety of reasons such as generalization performance, computational efficiency, and ...
Minh Hoai Nguyen, Fernando De la Torre
ICPR
2008
IEEE
15 years 3 days ago
Multiple kernel learning from sets of partially matching image features
Abstract: Kernel classifiers based on Support Vector Machines (SVM) have achieved state-ofthe-art results in several visual classification tasks, however, recent publications and d...
Guo ShengYang, Min Tan, Si-Yao Fu, Zeng-Guang Hou,...