Functional Regularized Least Squares Classi cation with Operator-valued Kernels
28th International Conference on Machine Learning (ICML), Seattle : United States (2011) Although operator-valued kernels have recently received increasing interest in various machine learning and functional data analysis problems such as multi-task learning or functional regression, little attentio...
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | Kadri, Hachem Rabaoui, Asma Preux, Philippe Duflos, Emmanuel Rakotomamonjy, Alain |
| description | 28th International Conference on Machine Learning (ICML), Seattle
: United States (2011) Although operator-valued kernels have recently received increasing interest
in various machine learning and functional data analysis problems such as
multi-task learning or functional regression, little attention has been paid to
the understanding of their associated feature spaces. In this paper, we explore
the potential of adopting an operator-valued kernel feature space perspective
for the analysis of functional data. We then extend the Regularized Least
Squares Classification (RLSC) algorithm to cover situations where there are
multiple functions per observation. Experiments on a sound recognition problem
show that the proposed method outperforms the classical RLSC algorithm. |
| doi_str_mv | 10.48550/arxiv.1301.2655 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_1301_2655</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1301_2655</sourcerecordid><originalsourceid>FETCH-LOGICAL-a655-d82efeef4af8651656eafb097a6dab2ba2ef38096c687b5bc4a44701a3165ce73</originalsourceid><addsrcrecordid>eNotj7tOwzAUhr0woMLOhPwCCU58iTuiiAIiUiToHh07x60lkxY7KZenJ6FM__Df9BFyU7BcaCnZHcQvf8oLzoq8VFJeknYzDXb0hwECfcXdFCD6H-xpg5BG-vYxQcRE6wApeWphSdJPP-5pe8QI4yFmJwjTXHjBOGBIV-TCQUh4_a8rst08bOunrGkfn-v7JoP5Nut1iQ7RCXBayUJJheAMW1egejClgdnmmq2VVboy0lgBQlSsAD5nLVZ8RW7Ps39A3TH6d4jf3QLWLWD8Fw4tSf0</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Functional Regularized Least Squares Classi cation with Operator-valued Kernels</title><source>arXiv.org</source><creator>Kadri, Hachem ; Rabaoui, Asma ; Preux, Philippe ; Duflos, Emmanuel ; Rakotomamonjy, Alain</creator><creatorcontrib>Kadri, Hachem ; Rabaoui, Asma ; Preux, Philippe ; Duflos, Emmanuel ; Rakotomamonjy, Alain</creatorcontrib><description>28th International Conference on Machine Learning (ICML), Seattle
: United States (2011) Although operator-valued kernels have recently received increasing interest
in various machine learning and functional data analysis problems such as
multi-task learning or functional regression, little attention has been paid to
the understanding of their associated feature spaces. In this paper, we explore
the potential of adopting an operator-valued kernel feature space perspective
for the analysis of functional data. We then extend the Regularized Least
Squares Classification (RLSC) algorithm to cover situations where there are
multiple functions per observation. Experiments on a sound recognition problem
show that the proposed method outperforms the classical RLSC algorithm.</description><identifier>DOI: 10.48550/arxiv.1301.2655</identifier><language>eng</language><subject>Computer Science - Learning ; Statistics - Machine Learning</subject><creationdate>2013-01</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1301.2655$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1301.2655$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Kadri, Hachem</creatorcontrib><creatorcontrib>Rabaoui, Asma</creatorcontrib><creatorcontrib>Preux, Philippe</creatorcontrib><creatorcontrib>Duflos, Emmanuel</creatorcontrib><creatorcontrib>Rakotomamonjy, Alain</creatorcontrib><title>Functional Regularized Least Squares Classi cation with Operator-valued Kernels</title><description>28th International Conference on Machine Learning (ICML), Seattle
: United States (2011) Although operator-valued kernels have recently received increasing interest
in various machine learning and functional data analysis problems such as
multi-task learning or functional regression, little attention has been paid to
the understanding of their associated feature spaces. In this paper, we explore
the potential of adopting an operator-valued kernel feature space perspective
for the analysis of functional data. We then extend the Regularized Least
Squares Classification (RLSC) algorithm to cover situations where there are
multiple functions per observation. Experiments on a sound recognition problem
show that the proposed method outperforms the classical RLSC algorithm.</description><subject>Computer Science - Learning</subject><subject>Statistics - Machine Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj7tOwzAUhr0woMLOhPwCCU58iTuiiAIiUiToHh07x60lkxY7KZenJ6FM__Df9BFyU7BcaCnZHcQvf8oLzoq8VFJeknYzDXb0hwECfcXdFCD6H-xpg5BG-vYxQcRE6wApeWphSdJPP-5pe8QI4yFmJwjTXHjBOGBIV-TCQUh4_a8rst08bOunrGkfn-v7JoP5Nut1iQ7RCXBayUJJheAMW1egejClgdnmmq2VVboy0lgBQlSsAD5nLVZ8RW7Ps39A3TH6d4jf3QLWLWD8Fw4tSf0</recordid><startdate>20130112</startdate><enddate>20130112</enddate><creator>Kadri, Hachem</creator><creator>Rabaoui, Asma</creator><creator>Preux, Philippe</creator><creator>Duflos, Emmanuel</creator><creator>Rakotomamonjy, Alain</creator><scope>AKY</scope><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20130112</creationdate><title>Functional Regularized Least Squares Classi cation with Operator-valued Kernels</title><author>Kadri, Hachem ; Rabaoui, Asma ; Preux, Philippe ; Duflos, Emmanuel ; Rakotomamonjy, Alain</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a655-d82efeef4af8651656eafb097a6dab2ba2ef38096c687b5bc4a44701a3165ce73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Computer Science - Learning</topic><topic>Statistics - Machine Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Kadri, Hachem</creatorcontrib><creatorcontrib>Rabaoui, Asma</creatorcontrib><creatorcontrib>Preux, Philippe</creatorcontrib><creatorcontrib>Duflos, Emmanuel</creatorcontrib><creatorcontrib>Rakotomamonjy, Alain</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Statistics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kadri, Hachem</au><au>Rabaoui, Asma</au><au>Preux, Philippe</au><au>Duflos, Emmanuel</au><au>Rakotomamonjy, Alain</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Functional Regularized Least Squares Classi cation with Operator-valued Kernels</atitle><date>2013-01-12</date><risdate>2013</risdate><abstract>28th International Conference on Machine Learning (ICML), Seattle
: United States (2011) Although operator-valued kernels have recently received increasing interest
in various machine learning and functional data analysis problems such as
multi-task learning or functional regression, little attention has been paid to
the understanding of their associated feature spaces. In this paper, we explore
the potential of adopting an operator-valued kernel feature space perspective
for the analysis of functional data. We then extend the Regularized Least
Squares Classification (RLSC) algorithm to cover situations where there are
multiple functions per observation. Experiments on a sound recognition problem
show that the proposed method outperforms the classical RLSC algorithm.</abstract><doi>10.48550/arxiv.1301.2655</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.1301.2655 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_1301_2655 |
| source | arXiv.org |
| subjects | Computer Science - Learning Statistics - Machine Learning |
| title | Functional Regularized Least Squares Classi cation with Operator-valued Kernels |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-12T19%3A36%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Functional%20Regularized%20Least%20Squares%20Classi%20cation%20with%20Operator-valued%20Kernels&rft.au=Kadri,%20Hachem&rft.date=2013-01-12&rft_id=info:doi/10.48550/arxiv.1301.2655&rft_dat=%3Carxiv_GOX%3E1301_2655%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |