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Author Brouard, C.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2012
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Calibration ♦ Learning systems ♦ Support vector machines ♦ Context ♦ Encyclopedias ♦ Electronic publishing ♦ relevance models ♦ classification ♦ neural network
Abstract In this paper we present a new classification system called ECHO. This system is based on a principle of echo and applied to document classification. It computes the score of a document for a class by combining a bottom-up and a top-down propagation of activation in a very simple neural network. This system bridges a gap between Machine Learning methods and Information Retrieval since the bottom-up and the top-down propagations can be seen as the measures of the specificity and exhaustivity which underlie the models of relevance used in Information Retrieval. The system has been tested on the Reuters 21578 collection and in the context of an international challenge on large scale hierarchical text classification with corpus extracted from Dmoz and Wikipedia. Its comparison with other classification systems has shown its efficiency.
Description Author affiliation: LIG, AMA Team, UPMF - Grenoble2, Grenoble, France (Brouard, C.)
ISBN 9781479902279
ISSN 10823409
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2012-11-07
Publisher Place Greece
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 328.15 kB
Page Count 7
Starting Page 735
Ending Page 741


Source: IEEE Xplore Digital Library