Relevance feedback with active learning for document retrieval
Takashi Onoda, Hideyuki Murata, Seiji Yamada · 2004
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 interesting in 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 interesting. We apply active learning techniques based on Support Vector Machine for evaluating successive batches, which is called relevance feedback. Finally, our proposed approach is very useful for document retrieval with relevance feedback experimentally.