Sheedvash, Sassan, authorSrinivasan, S. (Srini), authorSalazar, Jaime, authorAzimi-Sadjadi, Mahmood R., authorIEEE, publisher2007-01-032007-01-032007Azimi-Sadjadi, M. R., et al., An Adaptable Connectionist Text-Retrieval System with Relevance Feedback, IEEE Transactions on Neural Networks 18, no. 6 (November 2007): 1597-1613.http://hdl.handle.net/10217/931This paper introduces a new connectionist network for certain domain-specific text-retrieval and search applications with expert end users. A new model reference adaptive system is proposed that involves three learning phases. Initial model-reference learning is first performed based upon an ensemble set of input-output of an initial reference model. Model-reference following is needed in dynamic environments where documents are added, deleted, or updated. Relevance feedback learning from multiple expert users then optimally maps the original query using either a score-based or a click-through selection process. The learning can be implemented, in regression or classification modes, using a three-layer network. The first layer is an adaptable layer that performs mapping from query domain to document space. The second and third layers perform document-to-term mapping, search/retrieval, and scoring tasks. The learning algorithms are thoroughly tested on a domain-specific text database that encompasses a wide range of Hewlett Packard (HP) products and for a large number of most commonly used single- and multiterm queries.born digitalarticleseng©2007 IEEE.Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.query mappinglearning algorithmsconnectionist networksrelevance feedbacktext retrievalText