Abstract: At present sequential minimal optimization （SMO） algorithm is a very efficient method for training support vector machines （SVM）. However, the training speed of SMO is very slow for the large-scale datasets. Analyzing the varieties of the objective functi

Key words: Support vector machine, Sequential minimal optimization algorithm