Efficient SVM Regression Training with SMO
Machine Learning
A Simple Decomposition Method for Support Vector Machines
Machine Learning
Incremental Support Vector Machine Construction
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
One-class svms for document classification
The Journal of Machine Learning Research
Least squares twin parametric-margin support vector machine for classification
Applied Intelligence
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This paper proves the problem of losing incremental samples' information of the present SVM incremental learning algorithm from both theoretic and experimental aspects, and proposes a new incremental learning algorithm with support vector machine based on hyperplane-distance. According to the geometric character of support vector, the algorithm uses Hyperplane-Distance to extract the samples, selects samples which are most likely to become support vector to form the vector set of edge, and conducts the support vector machine training on the vector set. This method reduces the number of training samples and effectively improves training speed of incremental learning. The results of experiment performed on Chinese webpage classification show that this algorithm can reduce the number of training samples effectively and accumulate historical information. The HD-SVM algorithm has higher training speed and better precision of classification.