Non-Relevance Feedback Document Retrieval based on One Class SVM and SVDD

— This paper reports a new document retrieval method using non-relevant documents. Especially, this paper reports a comparison of retrieval efficiency between One Class Support vector Machine(SVM) based and Support Vector Data Description(SVDD) based interactive document retrieval method using non-relevant documents only. 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 applied 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. The traditional relevance feedback needs a set of relevant and non-relevant documents to work usefully. However, the initial retrieved documents, which are ...

• Type: Conference
• Year: 2006
• Where: IJCNN
• Authors: Takashi Onoda, Hiroshi Murata, Seiji Yamada
• Keywords: Documents, IJCNN 2006, Non-relevant Documents, Support Vector