Document space dimension reduction by nonlinear Hebbian neural network
This paper deals with information retrieval of text documents, and their clustering into some other feature space. The aim of this paper is to reduce the dimension of the document space by the nonlinear Hebbian neural network. As can be seen from the results, not only dimension reduction of document...
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| creator | Skovajsova, L. Mokris, I. |
| description | This paper deals with information retrieval of text documents, and their clustering into some other feature space. The aim of this paper is to reduce the dimension of the document space by the nonlinear Hebbian neural network. As can be seen from the results, not only dimension reduction of document space is made, but also clustering of these documents into clusters. We used here the nonlinear Hebbian neural network, which is feed-forward neural network with unsupervised learning. |
| doi_str_mv | 10.1109/SAMI.2009.4956615 |
| format | Conference Proceeding |
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The aim of this paper is to reduce the dimension of the document space by the nonlinear Hebbian neural network. As can be seen from the results, not only dimension reduction of document space is made, but also clustering of these documents into clusters. We used here the nonlinear Hebbian neural network, which is feed-forward neural network with unsupervised learning.</description><identifier>ISBN: 1424438012</identifier><identifier>ISBN: 9781424438013</identifier><identifier>EISBN: 9781424438020</identifier><identifier>EISBN: 1424438020</identifier><identifier>DOI: 10.1109/SAMI.2009.4956615</identifier><identifier>LCCN: 2009900281</identifier><language>eng</language><publisher>IEEE</publisher><subject>Feedforward neural networks ; Feedforward systems ; Indexing ; Information retrieval ; Matrix decomposition ; Neural networks ; Principal component analysis ; Sparse matrices ; Strontium ; Unsupervised learning</subject><ispartof>2009 7th International Symposium on Applied Machine Intelligence and Informatics, 2009, p.89-91</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4956615$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27923,54918</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4956615$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Skovajsova, L.</creatorcontrib><creatorcontrib>Mokris, I.</creatorcontrib><title>Document space dimension reduction by nonlinear Hebbian neural network</title><title>2009 7th International Symposium on Applied Machine Intelligence and Informatics</title><addtitle>SAMI</addtitle><description>This paper deals with information retrieval of text documents, and their clustering into some other feature space. The aim of this paper is to reduce the dimension of the document space by the nonlinear Hebbian neural network. As can be seen from the results, not only dimension reduction of document space is made, but also clustering of these documents into clusters. We used here the nonlinear Hebbian neural network, which is feed-forward neural network with unsupervised learning.</description><subject>Feedforward neural networks</subject><subject>Feedforward systems</subject><subject>Indexing</subject><subject>Information retrieval</subject><subject>Matrix decomposition</subject><subject>Neural networks</subject><subject>Principal component analysis</subject><subject>Sparse matrices</subject><subject>Strontium</subject><subject>Unsupervised learning</subject><isbn>1424438012</isbn><isbn>9781424438013</isbn><isbn>9781424438020</isbn><isbn>1424438020</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1UF9LwzAcjMhAN_sBxJd8gdZfmj9NHsd0bjDxwb2PNPkVol060hbZt7fDeS93B8dxHCGPDArGwDx_Lt-3RQlgCmGkUkzekMxUmolSCK6hhFsy_zesnJH5JWsASs3uSNb3XzBBSC4rcU_WL50bjxgH2p-sQ-rDZPrQRZrQj264qPpMYxfbENEmusG6DjbSiGOy7UTDT5e-H8issW2P2ZUXZL9-3a82-e7jbbta7vJgYMgbYY2sUBlwUlqtVSOVYNKjnBYxbaHy3DWGu6phnFs_JR2Cxdo0RpWe8wV5-qsNiHg4pXC06Xy43sB_ASChTus</recordid><startdate>200901</startdate><enddate>200901</enddate><creator>Skovajsova, L.</creator><creator>Mokris, I.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200901</creationdate><title>Document space dimension reduction by nonlinear Hebbian neural network</title><author>Skovajsova, L. ; Mokris, I.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-f4a957e690c55a886f56415de500018a07d3cf93c7f133ade69ce0aeb9f962d33</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Feedforward neural networks</topic><topic>Feedforward systems</topic><topic>Indexing</topic><topic>Information retrieval</topic><topic>Matrix decomposition</topic><topic>Neural networks</topic><topic>Principal component analysis</topic><topic>Sparse matrices</topic><topic>Strontium</topic><topic>Unsupervised learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Skovajsova, L.</creatorcontrib><creatorcontrib>Mokris, I.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Skovajsova, L.</au><au>Mokris, I.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Document space dimension reduction by nonlinear Hebbian neural network</atitle><btitle>2009 7th International Symposium on Applied Machine Intelligence and Informatics</btitle><stitle>SAMI</stitle><date>2009-01</date><risdate>2009</risdate><spage>89</spage><epage>91</epage><pages>89-91</pages><isbn>1424438012</isbn><isbn>9781424438013</isbn><eisbn>9781424438020</eisbn><eisbn>1424438020</eisbn><abstract>This paper deals with information retrieval of text documents, and their clustering into some other feature space. The aim of this paper is to reduce the dimension of the document space by the nonlinear Hebbian neural network. As can be seen from the results, not only dimension reduction of document space is made, but also clustering of these documents into clusters. We used here the nonlinear Hebbian neural network, which is feed-forward neural network with unsupervised learning.</abstract><pub>IEEE</pub><doi>10.1109/SAMI.2009.4956615</doi><tpages>3</tpages></addata></record> |
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| ispartof | 2009 7th International Symposium on Applied Machine Intelligence and Informatics, 2009, p.89-91 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Feedforward neural networks Feedforward systems Indexing Information retrieval Matrix decomposition Neural networks Principal component analysis Sparse matrices Strontium Unsupervised learning |
| title | Document space dimension reduction by nonlinear Hebbian neural network |
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