SVM learning from large training data set

Support vector machines have been gaining popularity in the research community of pattern classification. In this paper, we investigate efficient and effective algorithms for training SVMs on large data collections. We decompose SVM learning problem into two stages. At the first stage we developed an algorithm that uses a sequence of small subsets of training data to select the parameters γ and C. At the second stage, we developed an algorithm that generates a reliable set of support vectors using a small subset of the training data. Experiments are conducted on about 850K data samples for automotive engine misfire detection.