{"id":"https://openalex.org/W1967849786","doi":"https://doi.org/10.1145/1015330.1015426","title":"Multi-task feature and kernel selection for SVMs","display_name":"Multi-task feature and kernel selection for SVMs","publication_year":2004,"publication_date":"2004-01-01","ids":{"openalex":"https://openalex.org/W1967849786","doi":"https://doi.org/10.1145/1015330.1015426","mag":"1967849786"},"language":"en","primary_location":{"id":"doi:10.1145/1015330.1015426","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1015330.1015426","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Twenty-first international conference on Machine learning  - ICML '04","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068077704","display_name":"Tony Jebara","orcid":"https://orcid.org/0000-0003-0314-3376"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tony Jebara","raw_affiliation_strings":["Columbia University, New York, NY","Columbia University, New York, NY;"],"affiliations":[{"raw_affiliation_string":"Columbia University, New York, NY","institution_ids":["https://openalex.org/I78577930"]},{"raw_affiliation_string":"Columbia University, New York, NY;","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5068077704"],"corresponding_institution_ids":["https://openalex.org/I78577930"],"apc_list":null,"apc_paid":null,"fwci":4.6931,"has_fulltext":false,"cited_by_count":223,"citation_normalized_percentile":{"value":0.9528106,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"55","last_page":"55"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7885591983795166},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7011682391166687},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6364874839782715},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6353384256362915},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6310331225395203},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5731241106987},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.560484766960144},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.497482568025589},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.49518170952796936},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.49517813324928284},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.4494101107120514},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4302712678909302},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.416655033826828},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3340531289577484},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.263389527797699}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7885591983795166},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7011682391166687},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6364874839782715},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6353384256362915},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6310331225395203},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5731241106987},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.560484766960144},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.497482568025589},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.49518170952796936},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.49517813324928284},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.4494101107120514},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4302712678909302},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.416655033826828},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3340531289577484},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.263389527797699},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/1015330.1015426","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1015330.1015426","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Twenty-first international conference on Machine learning  - ICML '04","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.2.2337","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.2.2337","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.aicml.cs.ualberta.ca/banff04/icml/pages/papers/329.ps","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.218.7782","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.218.7782","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www1.cs.columbia.edu/~jebara/papers/metalearn.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6000000238418579,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W99485931","https://openalex.org/W1518171725","https://openalex.org/W1964514974","https://openalex.org/W2036043322","https://openalex.org/W2097839764","https://openalex.org/W2142387771","https://openalex.org/W2143104527","https://openalex.org/W2145295623","https://openalex.org/W2158078575","https://openalex.org/W2161813919","https://openalex.org/W2914746235","https://openalex.org/W3104240813","https://openalex.org/W3122943284"],"related_works":["https://openalex.org/W2089892314","https://openalex.org/W1603091392","https://openalex.org/W4386075310","https://openalex.org/W2169565408","https://openalex.org/W2127229869","https://openalex.org/W3123056048","https://openalex.org/W2150638158","https://openalex.org/W2121506664","https://openalex.org/W2363184354","https://openalex.org/W1975707885"],"abstract_inverted_index":{"We":[0,57],"compute":[1],"a":[2,36,45,59],"common":[3,37,46],"feature":[4],"selection":[5,8],"or":[6,50,100],"kernel":[7],"configuration":[9],"for":[10,53],"multiple":[11,27,87],"support":[12,80],"vector":[13,81],"machines":[14],"(SVMs)":[15],"trained":[16],"on":[17,108],"different":[18],"yet":[19],"inter-related":[20],"datasets.":[21,110],"The":[22,70],"method":[23],"is":[24],"advantageous":[25],"when":[26],"classification":[28],"tasks":[29],"and":[30],"differently":[31],"labeled":[32],"datasets":[33,41],"exist":[34],"over":[35],"input":[38],"space.":[39],"Different":[40],"can":[42],"mutually":[43],"reinforce":[44],"choice":[47],"of":[48,79,98,103],"representation":[49,61],"relevant":[51],"features":[52,99],"their":[54],"various":[55],"classifiers.":[56],"derive":[58],"multi-task":[60],"learning":[62],"approach":[63],"using":[64],"the":[65,75],"maximum":[66],"entropy":[67],"discrimination":[68],"formalism.":[69],"resulting":[71],"convex":[72],"algorithms":[73],"maintain":[74],"global":[76],"solution":[77],"properties":[78],"machines.":[82],"However,":[83],"in":[84],"addition":[85],"to":[86],"SVM":[88],"classification/regression":[89],"parameters":[90],"they":[91],"also":[92],"jointly":[93],"estimate":[94],"an":[95],"optimal":[96,101],"subset":[97],"combination":[102],"kernels.":[104],"Experiments":[105],"are":[106],"shown":[107],"standardized":[109]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":10},{"year":2018,"cited_by_count":17},{"year":2017,"cited_by_count":12},{"year":2016,"cited_by_count":9},{"year":2015,"cited_by_count":15},{"year":2014,"cited_by_count":12},{"year":2013,"cited_by_count":14},{"year":2012,"cited_by_count":17}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
