Document classification by computing an echo in a very simple neural network
|Document classification by computing an echo in a very simple neural network|
|Published in||Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI|
|Keyword(s)||classification, neural network, relevance models (Extra: Classification system, Document Classification, Its efficiencies, Machine learning methods, Relevance models, Reuters-21578, Text classification, Wikipedia, Classification (of information), Crack propagation, Information retrieval, Learning systems, Neural networks, Information retrieval systems)|
|Article||BASE, CiteSeerX, Google Scholar|
|Web||Ask, Bing, Google (PDF), Yahoo!|
|Download and mirrors|
|Local copy||Not available|
|Remote mirror(s)||Not available|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of conference papers|
Document classification by computing an echo in a very simple neural network is a 2012 conference paper written in English by Brouard C. and published in Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI.
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.
- This section requires expansion. Please, help!
Probably, this publication is cited by others, but there are no articles available for them in WikiPapers.