Relevance Feedback Algorithms Inspired By Quantum Detection - 2016 PROJECT TITLE: Relevance Feedback Algorithms Inspired By Quantum Detection - 2016 ABSTRACT: Information Retrieval (IR) is worried with indexing and retrieving documents as well as data relevant to a user's information want. Relevance Feedback (RF) could be a class of effective algorithms for improving Info Retrieval (IR) and it consists of gathering more data representing the user's info would like and automatically creating a replacement query. During this paper, we tend to propose a class of RF algorithms galvanized by quantum detection to re-weight the query terms and to re-rank the document retrieved by an IR system. These algorithms project the question vector on a subspace spanned by the eigenvector that maximizes the space between the distribution of quantum chance of relevance and the distribution of quantum probability of non-relevance. The experiments showed that the RF algorithms inspired by quantum detection can outperform the state-of-the-art algorithms. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest SecRBAC Secure data in the Clouds - 2016 Privacy-Preserving Outsourced Media Search - 2016
