A heuristic algorithm to incremental support vector machine learning

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Incremental learning techniques are possible solutions to handle vast data as information from Internet updating gets faster. Support vector machine works well for incremental learning model with impressive performance for its outstanding power to summarize the data space in a concise way. This paper proposes a heuristic algorithm to incremental learning with SVM taking the possible impact of new training data to history data into account. The idea of this heuristic algorithm is that the partition difference set has less elements, and existing hyperplane is much closer to the optimal one. New support vectors in this algorithm consist of existing support vectors and partition difference set of new training data and history data by separating hyperplane. The algorithm improves classification precision by adding partition difference set, and decreases the computation complexity by constructing new classification hyperplane on support vector set. The experimental results show that this heuristic algorithm is efficient and effective to improve the classification precision.