The nature of statistical learning theory
The nature of statistical learning theory
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Machine Learning
IEEE Intelligent Systems
Pattern Selection for Support Vector Classifiers
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
SVM-KM: Speeding SVMs Learning with a priori Cluster Selection and k-Means
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
The training of neural classifiers with condensed datasets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Sample selection via clustering to construct support vector-like classifiers
IEEE Transactions on Neural Networks
Invariance of neighborhood relation under input space to feature space mapping
Pattern Recognition Letters
Data Selection Using SASH Trees for Support Vector Machines
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Selecting Samples and Features for SVM Based on Neighborhood Model
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Closest pairs data selection for support vector machines
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
A Competitive Learning Approach to Instance Selection for Support Vector Machines
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
Using the leader algorithm with support vector machines for large data sets
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
ϵ-Tube based pattern selection for support vector machines
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Training data selection for support vector machines
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
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Training SVM requires large memory and long cpu time when the pattern set is large. To alleviate the computational burden in SVM training, we propose a fast preprocessing algorithm which selects only the patterns near the decision boundary. Preliminary simulation results were promising: Up to two orders of magnitude, training time reduction was achieved including the preprocessing, without any loss in classification accuracies.