Support vector machines for information retrieval

To solve the problem of the low sampling efficiency in the content-based information retrieval, a new classifier named 1.5-class support vector classifier (1.5 SVC) is proposed. To improve the detection rate, the positive samples are adopted to model the initial boundaries, and the available negative samples are added to refine the boundaries on the basis of keeping a good global generalization performance. The fast training algorithm of the method is also given by contrast with standard sequential minimal optimization. Compared with the traditional support vector classifiers, the experimental results on USPS and CBCL database demonstrate that the new classifier yields a higher detection rate.