A new semi-supervised support vector machine learning algorithm based on active learning

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Abstract

Semi-supervised support vector machine is an extension of standard support vector machine in machine learning problem in real life. However, the existing semi-supervised support vector machine algorithm has some drawbacks such as slower training speed, lower accuracy, etc. This paper presents a semi-supervised support vector machine learning algorithm based on active learning, which trains early learner by a spot of labeled-data, selects the best training samples for training and learning by active learning and reduces learning cost by deleting non- support vector. Simulative experiments have shown that the algorithm may get good learning effect at less learning cost.

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