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ECML
2004
Springer
14 years 3 months ago
Applying Support Vector Machines to Imbalanced Datasets
Support Vector Machines (SVM) have been extensively studied and have shown remarkable success in many applications. However the success of SVM is very limited when it is applied to...
Rehan Akbani, Stephen Kwek, Nathalie Japkowicz
ICMCS
2000
IEEE
170views Multimedia» more  ICMCS 2000»
14 years 2 months ago
Update Relevant Image Weights for Content-Based Image Retrieval using Support Vector Machines
Relevance feedback [1] has been a powerful tool for interactive Content-Based Image Retrieval (CBIR). During the retrieval process, the user selects the most relevant images and p...
Qi Tian, Pengyu Hong, Thomas S. Huang
ANNPR
2006
Springer
14 years 1 months ago
Fast Training of Linear Programming Support Vector Machines Using Decomposition Techniques
Abstract. Decomposition techniques are used to speed up training support vector machines but for linear programming support vector machines (LP-SVMs) direct implementation of decom...
Yusuke Torii, Shigeo Abe
ESANN
2008
13 years 11 months ago
Comparison of sparse least squares support vector regressors trained in primal and dual
In our previous work, we have developed sparse least squares support vector regressors (sparse LS SVRs) trained in the primal form in the reduced empirical feature space. In this p...
Shigeo Abe
NIPS
2007
13 years 11 months ago
Parallelizing Support Vector Machines on Distributed Computers
Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability, we have developed a parallel ...
Edward Y. Chang, Kaihua Zhu, Hao Wang, Hongjie Bai...