HARAM : a Hierarchical ARAM Neural Network for Large-Scale Text Classification

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BENITES, Fernando, Elena SAPOZHNIKOVA, 2015. HARAM : a Hierarchical ARAM Neural Network for Large-Scale Text Classification. 15th IEEE International Conference on Data Mining Workshop (ICDMW 2015). Atlantic City, NJ, USA, 14. Nov 2015 - 17. Nov 2015. In: CUI, Peng, ed. and others. 15th IEEE International Conference on Data Mining Workshop : Proceedings ; 14–17 November 2015, Atlantic City, New Jersey. 15th IEEE International Conference on Data Mining Workshop (ICDMW 2015). Atlantic City, NJ, USA, 14. Nov 2015 - 17. Nov 2015. Los Alamitos, CA:IEEE, pp. 847-854. ISBN 978-1-4673-8493-3

@inproceedings{Benites2015-11HARAM-33471, title={HARAM : a Hierarchical ARAM Neural Network for Large-Scale Text Classification}, year={2015}, doi={10.1109/ICDMW.2015.14}, isbn={978-1-4673-8493-3}, address={Los Alamitos, CA}, publisher={IEEE}, booktitle={15th IEEE International Conference on Data Mining Workshop : Proceedings ; 14–17 November 2015, Atlantic City, New Jersey}, pages={847--854}, editor={Cui, Peng}, author={Benites, Fernando and Sapozhnikova, Elena} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/33471"> <dc:creator>Benites, Fernando</dc:creator> <dc:creator>Sapozhnikova, Elena</dc:creator> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2016-03-24T13:34:46Z</dc:date> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/33471"/> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2016-03-24T13:34:46Z</dcterms:available> <dc:contributor>Benites, Fernando</dc:contributor> <dc:language>eng</dc:language> <dcterms:issued>2015-11</dcterms:issued> <dcterms:abstract xml:lang="eng">With the rapid development of the Web, the need for text classification of large data volumes is permanently growing. Texts represented as bags-of-words possess usually very high dimensionality in the input space and often also in the output space if labeled with many categories. As a result, neural classifiers should be adapted to such large-scale data. We present here a well scalable extension to the fuzzy Adaptive Resonance Associative Map (ARAM) neural network which was specially developed for high-dimensional and large data. This extension aims at increasing the classification speed by adding an extra ART layer for clustering learned prototypes into large clusters. In this case the activation of all prototypes can be replaced by the activation of a small fraction of them, leading to a significant reduction of the classification time. This extension can be especially useful for multi-label classification tasks.</dcterms:abstract> <dcterms:title>HARAM : a Hierarchical ARAM Neural Network for Large-Scale Text Classification</dcterms:title> <dc:contributor>Sapozhnikova, Elena</dc:contributor> </rdf:Description> </rdf:RDF>

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