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» Maximal Discrepancy for Support Vector Machines
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JMLR
2010
123views more  JMLR 2010»
13 years 5 months ago
Maximum Relative Margin and Data-Dependent Regularization
Leading classification methods such as support vector machines (SVMs) and their counterparts achieve strong generalization performance by maximizing the margin of separation betw...
Pannagadatta K. Shivaswamy, Tony Jebara
ICDM
2008
IEEE
160views Data Mining» more  ICDM 2008»
14 years 1 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
ICASSP
2010
IEEE
13 years 7 months ago
Language recognition using deep-structured conditional random fields
We present a novel language identification technique using our recently developed deep-structured conditional random fields (CRFs). The deep-structured CRF is a multi-layer CRF mo...
Dong Yu, Shizhen Wang, Zahi Karam, Li Deng
CIVR
2008
Springer
245views Image Analysis» more  CIVR 2008»
13 years 9 months ago
Probabilistic optimized ranking for multimedia semantic concept detection via RVM
We present a probabilistic ranking-driven classifier for the detection of video semantic concept, such as airplane, building, etc. Most existing concept detection systems utilize ...
Yantao Zheng, Shi-Yong Neo, Tat-Seng Chua, Qi Tian
KDD
2003
ACM
180views Data Mining» more  KDD 2003»
14 years 7 months ago
Classifying large data sets using SVMs with hierarchical clusters
Support vector machines (SVMs) have been promising methods for classification and regression analysis because of their solid mathematical foundations which convey several salient ...
Hwanjo Yu, Jiong Yang, Jiawei Han