{"id":"https://openalex.org/W2167197286","doi":"https://doi.org/10.1145/1401890.1401942","title":"A sequential dual method for large scale multi-class linear svms","display_name":"A sequential dual method for large scale multi-class linear svms","publication_year":2008,"publication_date":"2008-08-24","ids":{"openalex":"https://openalex.org/W2167197286","doi":"https://doi.org/10.1145/1401890.1401942","mag":"2167197286"},"language":"en","primary_location":{"id":"doi:10.1145/1401890.1401942","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1401890.1401942","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining","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/A5106708987","display_name":"S. Sathiya Keerthi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"S. Sathiya Keerthi","raw_affiliation_strings":["Yahoo! Research, Santa Clara, CA, USA"],"affiliations":[{"raw_affiliation_string":"Yahoo! Research, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I4210134091"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090165776","display_name":"S. Sundararajan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"S. Sundararajan","raw_affiliation_strings":["Yahoo! Labs, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"Yahoo! Labs, Bangalore, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073201681","display_name":"Kai\u2010Wei Chang","orcid":"https://orcid.org/0000-0002-4991-5274"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Kai-Wei Chang","raw_affiliation_strings":["National Taiwan University, Taipei, Taiwan, Roc"],"affiliations":[{"raw_affiliation_string":"National Taiwan University, Taipei, Taiwan, Roc","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010841999","display_name":"Cho\u2010Jui Hsieh","orcid":"https://orcid.org/0000-0002-3520-9627"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Cho-Jui Hsieh","raw_affiliation_strings":["National Taiwan University, Taipei, Taiwan, Roc"],"affiliations":[{"raw_affiliation_string":"National Taiwan University, Taipei, Taiwan, Roc","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068223640","display_name":"Chih\u2010Jen Lin","orcid":"https://orcid.org/0000-0003-4684-8747"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chih-Jen Lin","raw_affiliation_strings":["National Taiwan University, Taipei, Taiwan, Roc"],"affiliations":[{"raw_affiliation_string":"National Taiwan University, Taipei, Taiwan, Roc","institution_ids":["https://openalex.org/I16733864"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5106708987"],"corresponding_institution_ids":["https://openalex.org/I4210134091"],"apc_list":null,"apc_paid":null,"fwci":19.3574,"has_fulltext":false,"cited_by_count":153,"citation_normalized_percentile":{"value":0.99298246,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"408","last_page":"416"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9991000294685364,"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"}},"topics":[{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9991000294685364,"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/T10057","display_name":"Face and Expression Recognition","score":0.9948999881744385,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9789999723434448,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/traverse","display_name":"Traverse","score":0.8106034994125366},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.7054288387298584},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7006046175956726},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.6845085024833679},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6740311980247498},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5176506638526917},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4984128475189209},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4282749593257904},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.4123861789703369},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4117431640625},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41071659326553345},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33098816871643066},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.32175588607788086},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23463454842567444},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.08554229140281677}],"concepts":[{"id":"https://openalex.org/C176809094","wikidata":"https://www.wikidata.org/wiki/Q15401496","display_name":"Traverse","level":2,"score":0.8106034994125366},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.7054288387298584},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7006046175956726},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.6845085024833679},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6740311980247498},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5176506638526917},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4984128475189209},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4282749593257904},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.4123861789703369},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4117431640625},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41071659326553345},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33098816871643066},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.32175588607788086},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23463454842567444},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.08554229140281677},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1401890.1401942","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1401890.1401942","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.140.9389","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.9389","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.csie.ntu.edu.tw/~cjlin/papers/sdm_kdd.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W175058090","https://openalex.org/W1493526108","https://openalex.org/W1550206324","https://openalex.org/W1570978137","https://openalex.org/W1576520375","https://openalex.org/W1602492977","https://openalex.org/W1604938182","https://openalex.org/W1714704734","https://openalex.org/W2005688170","https://openalex.org/W2009593947","https://openalex.org/W2013850411","https://openalex.org/W2031248101","https://openalex.org/W2035720976","https://openalex.org/W2077658674","https://openalex.org/W2087347434","https://openalex.org/W2105842272","https://openalex.org/W2112796928","https://openalex.org/W2133864802","https://openalex.org/W2142623206","https://openalex.org/W2145276325","https://openalex.org/W2150024778","https://openalex.org/W2150102617","https://openalex.org/W2157791002","https://openalex.org/W2165966284","https://openalex.org/W2172000360","https://openalex.org/W2282078507","https://openalex.org/W3004533406","https://openalex.org/W3012317737","https://openalex.org/W3014813097","https://openalex.org/W3215084437","https://openalex.org/W4212997115","https://openalex.org/W4285719527","https://openalex.org/W6634442568","https://openalex.org/W6669838086"],"related_works":["https://openalex.org/W2377402383","https://openalex.org/W2380835401","https://openalex.org/W2381912691","https://openalex.org/W2350381577","https://openalex.org/W2353618196","https://openalex.org/W2348074676","https://openalex.org/W2385033175","https://openalex.org/W2374043190","https://openalex.org/W2363298784","https://openalex.org/W2367402697"],"abstract_inverted_index":{"Efficient":[0],"training":[1,48,65],"of":[2,6,64,84],"direct":[3],"multi-class":[4],"formulations":[5],"linear":[7],"Support":[8],"Vector":[9],"Machines":[10],"is":[11,42,66,79],"very":[12],"useful":[13],"in":[14],"applications":[15],"such":[16,88],"as":[17,25,27,89],"text":[18],"classification":[19],"with":[20,56],"a":[21,32,60],"huge":[22],"number":[23],"examples":[24],"well":[26],"features.":[28],"This":[29],"paper":[30],"presents":[31],"fast":[33],"dual":[34,53],"method":[35,78],"for":[36],"this":[37],"training.":[38],"The":[39,62],"main":[40],"idea":[41],"to":[43],"sequentially":[44],"traverse":[45],"through":[46],"the":[47,52,85],"set":[49],"and":[50,71,93],"optimize":[51],"variables":[54],"associated":[55],"one":[57],"example":[58],"at":[59],"time.":[61],"speed":[63],"enhanced":[67],"further":[68],"by":[69],"shrinking":[70],"cooling":[72],"heuristics.":[73],"Experiments":[74],"indicate":[75],"that":[76],"our":[77],"much":[80],"faster":[81],"than":[82],"state":[83],"art":[86],"solvers":[87],"bundle,":[90],"cutting":[91],"plane":[92],"exponentiated":[94],"gradient":[95],"methods.":[96]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":14},{"year":2014,"cited_by_count":18},{"year":2013,"cited_by_count":15},{"year":2012,"cited_by_count":23}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}