Relevance feedback document retrieval using support vector machines

We investigate the following data mining problems from the document retrieval: From a large data set of documents, we need to find documents that relate to human interest as few iterations of human testing or checking as possible. In each iteration a comparatively small batch of documents is evaluated for relating to the human interest. We apply active learning techniques based on support vector machine for evaluating successive batches, which is called relevance feedback. Our proposed approach has been very useful for document retrieval with relevance feedback experimentally. In this paper, we adopt several representations of the vector space model and several selecting rules of displayed documents at each iteration, and then show the comparison results of the effectiveness for the document retrieval in these several situations.