Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Incremental Support Vector Machine Construction
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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Support Vector Machine(SVM) has become a popular tool for learning with large amounts of high dimensional data, but sometimes we prefer to incremental learning algorithms to handle very vast data for training SVM is very costly in time and memory consumption or because the data available are obtained at different intervals. For its outstanding power to summarize the data space in a concise way, incremental SVM framework is designed to deal with large-scale learning problems. This paper proposes a gradual algorithm for training SVM to incremental learning in a dividable way, taking the possible impact of new training data to history data each other into account. Training data are divided and combined in a crossed way to collect support vectors, and being divided into smaller sets makes it easier to decreases the computation complexity and the gradual process can be trained in a parallel way. The experiment results on test dataset show that the classification accuracy using proposed incremental algorithm is superior to that using batch SVM model, the parallel training method is effective to decrease the training time consumption.