{"id":"https://openalex.org/W2020818097","doi":"https://doi.org/10.1145/1014052.1014089","title":"Incorporating prior knowledge with weighted margin support vector machines","display_name":"Incorporating prior knowledge with weighted margin support vector machines","publication_year":2004,"publication_date":"2004-08-22","ids":{"openalex":"https://openalex.org/W2020818097","doi":"https://doi.org/10.1145/1014052.1014089","mag":"2020818097"},"language":"en","primary_location":{"id":"doi:10.1145/1014052.1014089","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1014052.1014089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the tenth 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/A5100531509","display_name":"Wu Xiaoyun","orcid":"https://orcid.org/0009-0009-0180-5955"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xiaoyun Wu","raw_affiliation_strings":["University at Buffalo, Amherst, NY"],"affiliations":[{"raw_affiliation_string":"University at Buffalo, Amherst, NY","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063869451","display_name":"Rohini K. Srihari","orcid":"https://orcid.org/0000-0001-7454-6872"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rohini Srihari","raw_affiliation_strings":["University at Buffalo, Amherst, NY"],"affiliations":[{"raw_affiliation_string":"University at Buffalo, Amherst, NY","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100531509"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":13.0415,"has_fulltext":false,"cited_by_count":129,"citation_normalized_percentile":{"value":0.98675204,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"326","last_page":"333"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9990000128746033,"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":0.996399998664856,"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/support-vector-machine","display_name":"Support vector machine","score":0.8690732717514038},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.8347837924957275},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.750939130783081},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6966909170150757},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6955505013465881},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6706684827804565},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.46585991978645325},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.41869622468948364},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39116743206977844},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3295086622238159},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14410096406936646}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.8690732717514038},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.8347837924957275},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.750939130783081},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6966909170150757},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6955505013465881},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6706684827804565},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.46585991978645325},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.41869622468948364},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39116743206977844},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3295086622238159},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14410096406936646},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1014052.1014089","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1014052.1014089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1512098439","https://openalex.org/W1515450954","https://openalex.org/W1540550673","https://openalex.org/W1604792744","https://openalex.org/W1604938182","https://openalex.org/W1638608766","https://openalex.org/W1993644118","https://openalex.org/W1997029057","https://openalex.org/W2005422315","https://openalex.org/W2035754578","https://openalex.org/W2071664212","https://openalex.org/W2107008379","https://openalex.org/W2107968230","https://openalex.org/W2121649666","https://openalex.org/W2129894900","https://openalex.org/W2148603752","https://openalex.org/W2149684865","https://openalex.org/W2151040995","https://openalex.org/W2153635508","https://openalex.org/W2156909104","https://openalex.org/W2798909945","https://openalex.org/W6676348322","https://openalex.org/W6678226693","https://openalex.org/W7071374342"],"related_works":["https://openalex.org/W2603409575","https://openalex.org/W1554922784","https://openalex.org/W2314224212","https://openalex.org/W2345184372","https://openalex.org/W3013515612","https://openalex.org/W2136184105","https://openalex.org/W2187500075","https://openalex.org/W2041399278","https://openalex.org/W2160451891","https://openalex.org/W2336974148"],"abstract_inverted_index":{"Like":[0],"many":[1],"purely":[2],"data-driven":[3],"machine":[4],"learning":[5],"methods,":[6],"Support":[7],"Vector":[8],"Machine":[9],"(SVM)":[10],"classifiers":[11],"are":[12],"learned":[13],"exclusively":[14],"from":[15],"the":[16,20,48,68,87,103],"evidence":[17],"presented":[18],"in":[19,43,82],"training":[21,26,83],"dataset;":[22],"thus":[23],"a":[24,57],"larger":[25],"dataset":[27],"is":[28,110],"required":[29],"for":[30,47],"better":[31],"performance.":[32],"In":[33,52],"some":[34],"applications,":[35],"there":[36],"might":[37],"be":[38,80],"human":[39],"knowledge":[40,92,109],"available":[41],"that,":[42],"principle,":[44],"could":[45],"compensate":[46],"lack":[49],"of":[50,60,70,89,106],"data.":[51],"this":[53,94],"paper,":[54],"we":[55],"propose":[56],"simple":[58],"generalization":[59],"SVM:":[61],"Weighted":[62],"Margin":[63],"SVM":[64],"(WMSVMs)":[65],"that":[66,75,102],"permits":[67],"incorporation":[69],"prior":[71,91,108],"knowledge.":[72],"We":[73,85],"show":[74,101],"Sequential":[76],"Minimal":[77],"Optimization":[78],"can":[79],"used":[81],"WMSVM.":[84],"discuss":[86],"issues":[88],"incorporating":[90,107],"using":[93],"rather":[95],"general":[96],"formulation.":[97],"The":[98],"experimental":[99],"results":[100],"proposed":[104],"methods":[105],"effective.":[111]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":14},{"year":2012,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}