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IJCNN
2008
IEEE
15 years 7 months ago
Sparse support vector machines trained in the reduced empirical feature space
— We discuss sparse support vector machines (sparse SVMs) trained in the reduced empirical feature space. Namely, we select the linearly independent training data by the Cholesky...
Kazuki Iwamura, Shigeo Abe
75
Voted
ACMSE
2006
ACM
15 years 6 months ago
Support vector machines for collaborative filtering
Support Vector Machines (SVMs) have successfully shown efficiencies in many areas such as text categorization. Although recommendation systems share many similarities with text ca...
Zhonghang Xia, Yulin Dong, Guangming Xing
108
Voted
CAIP
2003
Springer
184views Image Analysis» more  CAIP 2003»
15 years 5 months ago
Multi-class Support Vector Machines with Case-Based Combination for Face Recognition
Abstract. The support vector machine is basically to deal with a two-class classification problem. To get M-class classifiers for face recognition, it is common to construct a set ...
Jaepil Ko, Hyeran Byun
JMLR
2006
150views more  JMLR 2006»
15 years 15 days ago
Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization
Support vector machines utilizing the 1-norm, typically set up as linear programs (Mangasarian, 2000; Bradley and Mangasarian, 1998), are formulated here as a completely unconstra...
Olvi L. Mangasarian
ICDM
2005
IEEE
161views Data Mining» more  ICDM 2005»
15 years 6 months ago
Making Logistic Regression a Core Data Mining Tool with TR-IRLS
Binary classification is a core data mining task. For large datasets or real-time applications, desirable classifiers are accurate, fast, and need no parameter tuning. We presen...
Paul Komarek, Andrew W. Moore