Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Pairwise classification and support vector machines
Advances in kernel methods
Pattern Classification: Neuro-Fuzzy Methods and Their Comparison
Pattern Classification: Neuro-Fuzzy Methods and Their Comparison
Training data selection for support vector machines
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Support vector machines training data selection using a genetic algorithm
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
| Hi-index | 0.00 |
Support vector machines have gotten wide acceptance for their high generalization ability for real world applications. But the major drawback is slow training for classification problems with a large number of training data. To overcome this problem, in this paper, we discuss extracting boundary data from the training data and train the support vector machine using only these data. Namely, for each training datum we calculate the Mahalanobis distances and extract those data that are misclassified by the Mahalanobis distances or that have small relative differences of the Mahalanobis distances. We demonstrate the effectiveness of the method for the benchmark data sets.