Making large-scale support vector machine learning practical
Advances in kernel methods
The generalized Bayesian committee machine
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Neural Computation
Fast Support Vector Data Description Using K-Means Clustering
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
A Confident Majority Voting Strategy for Parallel and Modular Support Vector Machines
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
A hierarchical and parallel method for training support vector machines
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
| Hi-index | 0.00 |
Empirical evidence indicates that the training time for the support vector machine (SVM) scales to the square of the number of training data points. In this paper, we introduce the Bayesian committee support vector machine (BC-SVM) and achieve an algorithm for training the SVM which scales linearly in the number of training data points. We verify the good performance of the BC-SVM using several data sets.