The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Probabilistic Active Support Vector Learning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Improved feature reduction in input and feature spaces
Pattern Recognition
A distance-based separator representation for pattern classification
Image and Vision Computing
An incremental learning algorithm for Lagrangian support vector machines
Pattern Recognition Letters
An online core vector machine with adaptive MEB adjustment
Pattern Recognition
Inconsistency-based active learning for support vector machines
Pattern Recognition
Inverse matrix-free incremental proximal support vector machine
Decision Support Systems
Granular support vector machine based on mixed measure
Neurocomputing
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In this paper, we present an improved incremental training algorithm for support vector machines (SVMs). Instead of selecting training samples randomly, we divide them into groups and apply the k-means clustering algorithm to collect the initial set of training samples. In active query, we assign a weight to each sample according to its confidence factor and its distance to the separating hyperplane. The confidence factor is calculated from the error upper bound of the SVM to indicate the closeness of the current hyperplane to the optimal hyperplane. A criterion is developed to eliminate non-informative training samples incrementally. Experimental results show our algorithm works successfully on artificial and real data, and is superior to the existing methods.