Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A parallel mixture of SVMs for very large scale problems
Neural Computation
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Visual query suggestion: Towards capturing user intent in internet image search
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Support vector machine classification based on fuzzy clustering for large data sets
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Interactive Video Indexing With Statistical Active Learning
IEEE Transactions on Multimedia
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Support Vector Machine (SVM) training in a large data set involves a huge optimization problem to make SVM impractical even for a moderate data set. In this paper, we propose a method to speed SVM training by removing redundant data in the training data set. The training data set is clustered firstly. Then the Fisher Discriminant Ratio is used to find the boundary between the clustered and scattered data points in one cluster, which is computed based on the distance density of data points to the cluster centroid. The clustered data points that lie around the clustering centroid are far away from the separating hyperplane, which are considered as the redundant data points and removed. While the scattered data points are in the outside layer of cluster, which are near the separating hyperplane and considered as having the potential Support Vectors (SVs) and reserved. Several experimental results show that our approach preserve good classification accuracy while the training time is reduced significantly.