Summary: IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 18, NO. 6, NOVEMBER 2007 1597
An Adaptable Connectionist Text-Retrieval System
With Relevance Feedback
M. R. Azimi-Sadjadi, Senior Member, IEEE, J. Salazar, S. Srinivasan, and S. Sheedvash
Abstract--This 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-ref-
erence learning is first performed based upon an ensemble set
of input­output 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 algo-
rithms are thoroughly tested on a domain-specific text database

Source: Azimi-Sadjadi, Mahmood R. - Department of Electrical and Computer Engineering, Colorado State University

Collections: Computer Technologies and Information Sciences