Nonlinear System Identification With Composite Relevance Vector Machines
Nonlinear system identification based on relevance vector machines (RVMs) has been traditionally addressed by stacking the input and/or output regressors and then performing standard RVM regression. This letter introduces a full family of composite kernels in order to integrate the input and output...
Gespeichert in:
| Veröffentlicht in: | IEEE signal processing letters 2007-04, Vol.14 (4), p.279-282 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 282 |
|---|---|
| container_issue | 4 |
| container_start_page | 279 |
| container_title | IEEE signal processing letters |
| container_volume | 14 |
| creator | Camps-Valls, G. Martinez-Ramon, M. Rojo-Alvarez, J.L. Munoz-Mari, J. |
| description | Nonlinear system identification based on relevance vector machines (RVMs) has been traditionally addressed by stacking the input and/or output regressors and then performing standard RVM regression. This letter introduces a full family of composite kernels in order to integrate the input and output information in the mapping function efficiently and hence generalize the standard approach. An improved trade-off between accuracy and sparsity is obtained in several benchmark problems. Also, the RVM yields confidence intervals for the predictions, and it is less sensitive to free parameter selection |
| doi_str_mv | 10.1109/LSP.2006.885290 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_miscellaneous_34449006</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4130389</ieee_id><sourcerecordid>903636115</sourcerecordid><originalsourceid>FETCH-LOGICAL-c425t-7e75a9ae7c6806373d1ba50a0618013b77b86a0620cad6626dcd40376a4606c53</originalsourceid><addsrcrecordid>eNp9kb1LA0EQxQ9RMEZrC5vDQm0uzt5-lxLUCPEDP8tlszchGy638fYi-N-7MWJhYTUz8HsP3rwsOyQwIAT0-fjpYVACiIFSvNSwlfUI56ooqSDbaQcJhdagdrO9GOcAoIjivWx0F5raN2jb_OkzdrjIbypsOj_1znY-NPmb72b5MCyWIfoO80es8cM2DvNXdF1o81vrZkkf97Odqa0jHvzMfvZydfk8HBXj--ub4cW4cKzkXSFRcqstSicUCCppRSaWgwVBFBA6kXKiRLpKcLYSohSVqxhQKSwTIByn_ex047tsw_sKY2cWPjqsa9tgWEWjgYoUmazJk39JyhjT6WEJPPsXJEISKqDUKqHHf9B5WLVNCmyUYEoz_u13voFcG2JscWqWrV_Y9tMQMOuuTOrKrLsym66S4mij8Ij4SzNCgSpNvwBg641n</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>864894506</pqid></control><display><type>article</type><title>Nonlinear System Identification With Composite Relevance Vector Machines</title><source>IEEE Electronic Library (IEL)</source><creator>Camps-Valls, G. ; Martinez-Ramon, M. ; Rojo-Alvarez, J.L. ; Munoz-Mari, J.</creator><creatorcontrib>Camps-Valls, G. ; Martinez-Ramon, M. ; Rojo-Alvarez, J.L. ; Munoz-Mari, J.</creatorcontrib><description>Nonlinear system identification based on relevance vector machines (RVMs) has been traditionally addressed by stacking the input and/or output regressors and then performing standard RVM regression. This letter introduces a full family of composite kernels in order to integrate the input and output information in the mapping function efficiently and hence generalize the standard approach. An improved trade-off between accuracy and sparsity is obtained in several benchmark problems. Also, the RVM yields confidence intervals for the predictions, and it is less sensitive to free parameter selection</description><identifier>ISSN: 1070-9908</identifier><identifier>EISSN: 1558-2361</identifier><identifier>DOI: 10.1109/LSP.2006.885290</identifier><identifier>CODEN: ISPLEM</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Bayesian methods ; Composite kernels ; Confidence intervals ; Desktop publishing ; Dynamical systems ; Function approximation ; Kernel ; Mathematical analysis ; Nonlinear dynamics ; nonlinear system identification ; Nonlinear systems ; Regression ; relevance vector machine (RVM) ; Signal processing algorithms ; Stacking ; Support vector machine classification ; Support vector machines ; System identification ; Tradeoffs ; Vectors (mathematics)</subject><ispartof>IEEE signal processing letters, 2007-04, Vol.14 (4), p.279-282</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2007</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c425t-7e75a9ae7c6806373d1ba50a0618013b77b86a0620cad6626dcd40376a4606c53</citedby><cites>FETCH-LOGICAL-c425t-7e75a9ae7c6806373d1ba50a0618013b77b86a0620cad6626dcd40376a4606c53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4130389$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4130389$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Camps-Valls, G.</creatorcontrib><creatorcontrib>Martinez-Ramon, M.</creatorcontrib><creatorcontrib>Rojo-Alvarez, J.L.</creatorcontrib><creatorcontrib>Munoz-Mari, J.</creatorcontrib><title>Nonlinear System Identification With Composite Relevance Vector Machines</title><title>IEEE signal processing letters</title><addtitle>LSP</addtitle><description>Nonlinear system identification based on relevance vector machines (RVMs) has been traditionally addressed by stacking the input and/or output regressors and then performing standard RVM regression. This letter introduces a full family of composite kernels in order to integrate the input and output information in the mapping function efficiently and hence generalize the standard approach. An improved trade-off between accuracy and sparsity is obtained in several benchmark problems. Also, the RVM yields confidence intervals for the predictions, and it is less sensitive to free parameter selection</description><subject>Bayesian methods</subject><subject>Composite kernels</subject><subject>Confidence intervals</subject><subject>Desktop publishing</subject><subject>Dynamical systems</subject><subject>Function approximation</subject><subject>Kernel</subject><subject>Mathematical analysis</subject><subject>Nonlinear dynamics</subject><subject>nonlinear system identification</subject><subject>Nonlinear systems</subject><subject>Regression</subject><subject>relevance vector machine (RVM)</subject><subject>Signal processing algorithms</subject><subject>Stacking</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><subject>System identification</subject><subject>Tradeoffs</subject><subject>Vectors (mathematics)</subject><issn>1070-9908</issn><issn>1558-2361</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kb1LA0EQxQ9RMEZrC5vDQm0uzt5-lxLUCPEDP8tlszchGy638fYi-N-7MWJhYTUz8HsP3rwsOyQwIAT0-fjpYVACiIFSvNSwlfUI56ooqSDbaQcJhdagdrO9GOcAoIjivWx0F5raN2jb_OkzdrjIbypsOj_1znY-NPmb72b5MCyWIfoO80es8cM2DvNXdF1o81vrZkkf97Odqa0jHvzMfvZydfk8HBXj--ub4cW4cKzkXSFRcqstSicUCCppRSaWgwVBFBA6kXKiRLpKcLYSohSVqxhQKSwTIByn_ex047tsw_sKY2cWPjqsa9tgWEWjgYoUmazJk39JyhjT6WEJPPsXJEISKqDUKqHHf9B5WLVNCmyUYEoz_u13voFcG2JscWqWrV_Y9tMQMOuuTOrKrLsym66S4mij8Ij4SzNCgSpNvwBg641n</recordid><startdate>20070401</startdate><enddate>20070401</enddate><creator>Camps-Valls, G.</creator><creator>Martinez-Ramon, M.</creator><creator>Rojo-Alvarez, J.L.</creator><creator>Munoz-Mari, J.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>20070401</creationdate><title>Nonlinear System Identification With Composite Relevance Vector Machines</title><author>Camps-Valls, G. ; Martinez-Ramon, M. ; Rojo-Alvarez, J.L. ; Munoz-Mari, J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c425t-7e75a9ae7c6806373d1ba50a0618013b77b86a0620cad6626dcd40376a4606c53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Bayesian methods</topic><topic>Composite kernels</topic><topic>Confidence intervals</topic><topic>Desktop publishing</topic><topic>Dynamical systems</topic><topic>Function approximation</topic><topic>Kernel</topic><topic>Mathematical analysis</topic><topic>Nonlinear dynamics</topic><topic>nonlinear system identification</topic><topic>Nonlinear systems</topic><topic>Regression</topic><topic>relevance vector machine (RVM)</topic><topic>Signal processing algorithms</topic><topic>Stacking</topic><topic>Support vector machine classification</topic><topic>Support vector machines</topic><topic>System identification</topic><topic>Tradeoffs</topic><topic>Vectors (mathematics)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Camps-Valls, G.</creatorcontrib><creatorcontrib>Martinez-Ramon, M.</creatorcontrib><creatorcontrib>Rojo-Alvarez, J.L.</creatorcontrib><creatorcontrib>Munoz-Mari, J.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE signal processing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Camps-Valls, G.</au><au>Martinez-Ramon, M.</au><au>Rojo-Alvarez, J.L.</au><au>Munoz-Mari, J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Nonlinear System Identification With Composite Relevance Vector Machines</atitle><jtitle>IEEE signal processing letters</jtitle><stitle>LSP</stitle><date>2007-04-01</date><risdate>2007</risdate><volume>14</volume><issue>4</issue><spage>279</spage><epage>282</epage><pages>279-282</pages><issn>1070-9908</issn><eissn>1558-2361</eissn><coden>ISPLEM</coden><abstract>Nonlinear system identification based on relevance vector machines (RVMs) has been traditionally addressed by stacking the input and/or output regressors and then performing standard RVM regression. This letter introduces a full family of composite kernels in order to integrate the input and output information in the mapping function efficiently and hence generalize the standard approach. An improved trade-off between accuracy and sparsity is obtained in several benchmark problems. Also, the RVM yields confidence intervals for the predictions, and it is less sensitive to free parameter selection</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/LSP.2006.885290</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1070-9908 |
| ispartof | IEEE signal processing letters, 2007-04, Vol.14 (4), p.279-282 |
| issn | 1070-9908 1558-2361 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_34449006 |
| source | IEEE Electronic Library (IEL) |
| subjects | Bayesian methods Composite kernels Confidence intervals Desktop publishing Dynamical systems Function approximation Kernel Mathematical analysis Nonlinear dynamics nonlinear system identification Nonlinear systems Regression relevance vector machine (RVM) Signal processing algorithms Stacking Support vector machine classification Support vector machines System identification Tradeoffs Vectors (mathematics) |
| title | Nonlinear System Identification With Composite Relevance Vector Machines |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-10T12%3A10%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Nonlinear%20System%20Identification%20With%20Composite%20Relevance%20Vector%20Machines&rft.jtitle=IEEE%20signal%20processing%20letters&rft.au=Camps-Valls,%20G.&rft.date=2007-04-01&rft.volume=14&rft.issue=4&rft.spage=279&rft.epage=282&rft.pages=279-282&rft.issn=1070-9908&rft.eissn=1558-2361&rft.coden=ISPLEM&rft_id=info:doi/10.1109/LSP.2006.885290&rft_dat=%3Cproquest_RIE%3E903636115%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=864894506&rft_id=info:pmid/&rft_ieee_id=4130389&rfr_iscdi=true |