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» Support Vector Machines: Theory and Applications
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KDD
1995
ACM
139views Data Mining» more  KDD 1995»
14 years 1 days ago
Extracting Support Data for a Given Task
We report a novel possibility for extracting a small subset of a data base which contains all the information necessary to solve a given classification task: using the Support Vec...
Bernhard Schölkopf, Chris Burges, Vladimir Va...
CVPR
2005
IEEE
14 years 2 months ago
Nonlinear Face Recognition Based on Maximum Average Margin Criterion
This paper proposes a novel nonlinear discriminant analysis method named by Kernerlized Maximum Average Margin Criterion (KMAMC), which has combined the idea of Support Vector Mac...
Baochang Zhang, Xilin Chen, Shiguang Shan, Wen Gao
ICPR
2008
IEEE
14 years 2 months ago
RANSAC-SVM for large-scale datasets
Support Vector Machines (SVMs), though accurate, are still difficult to solve large-scale applications, due to the computational and storage requirement. To relieve this problem,...
Kenji Watanabe, Takio Kurita
ICPR
2006
IEEE
14 years 9 months ago
Car/Non-Car Classification in an Informative Sample Subspace
In this paper, we present a method for data classification with application to car/non-car objects. We first developed a sample based car/non-car maximal mutual information low di...
Guoping Qiu, Jianzhong Fang
KDD
2009
ACM
156views Data Mining» more  KDD 2009»
14 years 9 months ago
Multi-focal learning and its application to customer service support
In this study, we formalize a multi-focal learning problem, where training data are partitioned into several different focal groups and the prediction model will be learned within...
Yong Ge, Hui Xiong, Wenjun Zhou, Ramendra K. Sahoo...