Fast multiple instance learning via L1,2 logistic regression
In this paper, we develop an efficient logistic regression model for multiple instance learning that combines L 1 and L 2 regularisation techniques. An L 1 regularised logistic regression model is first learned to find out the sparse pattern of the features. To train the L 1 model efficiently, we em...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 4 |
|---|---|
| container_issue | |
| container_start_page | 1 |
| container_title | |
| container_volume | |
| creator | Zhouyu Fu Robles-Kelly, A. |
| description | In this paper, we develop an efficient logistic regression model for multiple instance learning that combines L 1 and L 2 regularisation techniques. An L 1 regularised logistic regression model is first learned to find out the sparse pattern of the features. To train the L 1 model efficiently, we employ a convex differentiable approximation of the L 1 cost function which can be solved by a quasi Newton method. We then train an L 2 regularised logistic regression model only on the subset of features with nonzero weights returned by the L 1 logistic regression. Experimental results demonstrate the utility and efficiency of the proposed approach compared to a number of alternatives. |
| doi_str_mv | 10.1109/ICPR.2008.4761294 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_4761294</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4761294</ieee_id><sourcerecordid>4761294</sourcerecordid><originalsourceid>FETCH-LOGICAL-i90t-b9c6700b028aea80cfdb7211b447829789ca583597679b6e0c69798e7524f1783</originalsourceid><addsrcrecordid>eNpVkM1Kw0AURsc_sNY-gLiZBzDx3snM3BlwI8FqIaBI92USb8JImpZMFHx7C3bj6lscOBw-IW4QckTw96vy7T1XAC7XZFF5fSIWnhxqpbVCMvZUzJQrMCNN5uwf0_5czBAMZtoavBRXKX0CKCiMm4mHZUiT3H71U9z3LOOQpjA0LHsO4xCHTn7HICu8U7LfdTFNsZEjdyOnFHfDtbhoQ594cdy5WC-f1uVLVr0-r8rHKosepqz2jSWAGpQLHBw07UdNCrHWmpw6hPomGFcYT5Z8bRka68k7JqN0i-SKubj900Zm3uzHuA3jz-b4Q_EL9-JLFw</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Fast multiple instance learning via L1,2 logistic regression</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Zhouyu Fu ; Robles-Kelly, A.</creator><creatorcontrib>Zhouyu Fu ; Robles-Kelly, A.</creatorcontrib><description>In this paper, we develop an efficient logistic regression model for multiple instance learning that combines L 1 and L 2 regularisation techniques. An L 1 regularised logistic regression model is first learned to find out the sparse pattern of the features. To train the L 1 model efficiently, we employ a convex differentiable approximation of the L 1 cost function which can be solved by a quasi Newton method. We then train an L 2 regularised logistic regression model only on the subset of features with nonzero weights returned by the L 1 logistic regression. Experimental results demonstrate the utility and efficiency of the proposed approach compared to a number of alternatives.</description><identifier>ISSN: 1051-4651</identifier><identifier>ISBN: 9781424421749</identifier><identifier>ISBN: 1424421748</identifier><identifier>EISSN: 2831-7475</identifier><identifier>EISBN: 9781424421756</identifier><identifier>EISBN: 1424421756</identifier><identifier>DOI: 10.1109/ICPR.2008.4761294</identifier><language>eng</language><publisher>IEEE</publisher><subject>Australia ; Bandwidth ; Cost function ; Logistics ; Machine learning ; Newton method ; Optimization methods ; Supervised learning ; Support vector machine classification ; Support vector machines</subject><ispartof>2008 19th International Conference on Pattern Recognition, 2008, p.1-4</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/4761294$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4761294$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhouyu Fu</creatorcontrib><creatorcontrib>Robles-Kelly, A.</creatorcontrib><title>Fast multiple instance learning via L1,2 logistic regression</title><title>2008 19th International Conference on Pattern Recognition</title><addtitle>ICPR</addtitle><description>In this paper, we develop an efficient logistic regression model for multiple instance learning that combines L 1 and L 2 regularisation techniques. An L 1 regularised logistic regression model is first learned to find out the sparse pattern of the features. To train the L 1 model efficiently, we employ a convex differentiable approximation of the L 1 cost function which can be solved by a quasi Newton method. We then train an L 2 regularised logistic regression model only on the subset of features with nonzero weights returned by the L 1 logistic regression. Experimental results demonstrate the utility and efficiency of the proposed approach compared to a number of alternatives.</description><subject>Australia</subject><subject>Bandwidth</subject><subject>Cost function</subject><subject>Logistics</subject><subject>Machine learning</subject><subject>Newton method</subject><subject>Optimization methods</subject><subject>Supervised learning</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><issn>1051-4651</issn><issn>2831-7475</issn><isbn>9781424421749</isbn><isbn>1424421748</isbn><isbn>9781424421756</isbn><isbn>1424421756</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkM1Kw0AURsc_sNY-gLiZBzDx3snM3BlwI8FqIaBI92USb8JImpZMFHx7C3bj6lscOBw-IW4QckTw96vy7T1XAC7XZFF5fSIWnhxqpbVCMvZUzJQrMCNN5uwf0_5czBAMZtoavBRXKX0CKCiMm4mHZUiT3H71U9z3LOOQpjA0LHsO4xCHTn7HICu8U7LfdTFNsZEjdyOnFHfDtbhoQ594cdy5WC-f1uVLVr0-r8rHKosepqz2jSWAGpQLHBw07UdNCrHWmpw6hPomGFcYT5Z8bRka68k7JqN0i-SKubj900Zm3uzHuA3jz-b4Q_EL9-JLFw</recordid><startdate>200812</startdate><enddate>200812</enddate><creator>Zhouyu Fu</creator><creator>Robles-Kelly, A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200812</creationdate><title>Fast multiple instance learning via L1,2 logistic regression</title><author>Zhouyu Fu ; Robles-Kelly, A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-b9c6700b028aea80cfdb7211b447829789ca583597679b6e0c69798e7524f1783</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Australia</topic><topic>Bandwidth</topic><topic>Cost function</topic><topic>Logistics</topic><topic>Machine learning</topic><topic>Newton method</topic><topic>Optimization methods</topic><topic>Supervised learning</topic><topic>Support vector machine classification</topic><topic>Support vector machines</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhouyu Fu</creatorcontrib><creatorcontrib>Robles-Kelly, A.</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>Zhouyu Fu</au><au>Robles-Kelly, A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Fast multiple instance learning via L1,2 logistic regression</atitle><btitle>2008 19th International Conference on Pattern Recognition</btitle><stitle>ICPR</stitle><date>2008-12</date><risdate>2008</risdate><spage>1</spage><epage>4</epage><pages>1-4</pages><issn>1051-4651</issn><eissn>2831-7475</eissn><isbn>9781424421749</isbn><isbn>1424421748</isbn><eisbn>9781424421756</eisbn><eisbn>1424421756</eisbn><abstract>In this paper, we develop an efficient logistic regression model for multiple instance learning that combines L 1 and L 2 regularisation techniques. An L 1 regularised logistic regression model is first learned to find out the sparse pattern of the features. To train the L 1 model efficiently, we employ a convex differentiable approximation of the L 1 cost function which can be solved by a quasi Newton method. We then train an L 2 regularised logistic regression model only on the subset of features with nonzero weights returned by the L 1 logistic regression. Experimental results demonstrate the utility and efficiency of the proposed approach compared to a number of alternatives.</abstract><pub>IEEE</pub><doi>10.1109/ICPR.2008.4761294</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1051-4651 |
| ispartof | 2008 19th International Conference on Pattern Recognition, 2008, p.1-4 |
| issn | 1051-4651 2831-7475 |
| language | eng |
| recordid | cdi_ieee_primary_4761294 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Australia Bandwidth Cost function Logistics Machine learning Newton method Optimization methods Supervised learning Support vector machine classification Support vector machines |
| title | Fast multiple instance learning via L1,2 logistic regression |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T02%3A19%3A08IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Fast%20multiple%20instance%20learning%20via%20L1,2%20logistic%20regression&rft.btitle=2008%2019th%20International%20Conference%20on%20Pattern%20Recognition&rft.au=Zhouyu%20Fu&rft.date=2008-12&rft.spage=1&rft.epage=4&rft.pages=1-4&rft.issn=1051-4651&rft.eissn=2831-7475&rft.isbn=9781424421749&rft.isbn_list=1424421748&rft_id=info:doi/10.1109/ICPR.2008.4761294&rft_dat=%3Cieee_6IE%3E4761294%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781424421756&rft.eisbn_list=1424421756&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=4761294&rfr_iscdi=true |