{"id":"https://openalex.org/W2046513037","doi":"https://doi.org/10.1145/1015330.1015409","title":"A fast iterative algorithm for fisher discriminant using heterogeneous kernels","display_name":"A fast iterative algorithm for fisher discriminant using heterogeneous kernels","publication_year":2004,"publication_date":"2004-01-01","ids":{"openalex":"https://openalex.org/W2046513037","doi":"https://doi.org/10.1145/1015330.1015409","mag":"2046513037"},"language":"en","primary_location":{"id":"doi:10.1145/1015330.1015409","doi":"https://doi.org/10.1145/1015330.1015409","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1015330.1015409","pdf_url":null,"source":{"id":"https://openalex.org/S4306419644","display_name":"International Conference on Machine Learning","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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"},"volume":null,"type":"conference-abstract","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/A5111408590","display_name":"Glenn Fung","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Glenn Fung","raw_affiliation_strings":["Computer Aided Diagnosis & Therapy Solutions, Malvern PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Aided Diagnosis & Therapy Solutions, Malvern PA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067612292","display_name":"Murat D\u00fcndar","orcid":"https://orcid.org/0000-0001-5752-468X","observed_orcids":["https://orcid.org/0000-0001-5752-468X","https://orcid.org/0000-0002-3956-8088"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Murat Dundar","raw_affiliation_strings":["Computer Aided Diagnosis & Therapy Solutions, Malvern PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Aided Diagnosis & Therapy Solutions, Malvern PA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051393532","display_name":"Jinbo Bi","orcid":"https://orcid.org/0000-0001-6996-4092","observed_orcids":["https://orcid.org/0000-0001-6996-4092"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jinbo Bi","raw_affiliation_strings":["Computer Aided Diagnosis & Therapy Solutions, Malvern PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Aided Diagnosis & Therapy Solutions, Malvern PA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064671460","display_name":"Bharat Rao","orcid":"https://orcid.org/0000-0002-8037-0145","observed_orcids":["https://orcid.org/0000-0002-8037-0145"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bharat Rao","raw_affiliation_strings":["Computer Aided Diagnosis & Therapy Solutions, Malvern PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Aided Diagnosis & Therapy Solutions, Malvern PA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":50,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"40","last_page":"40"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13141","display_name":"Statistical Methods and Applications","score":0.3637999892234802,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13141","display_name":"Statistical Methods and Applications","score":0.3637999892234802,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.32670000195503235,"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":0.08110000193119049,"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/kernel-fisher-discriminant-analysis","display_name":"kernel Fisher discriminant analysis","score":0.9729999899864197},{"id":"https://openalex.org/keywords/kernel-selection","display_name":"kernel selection","score":0.8679999709129333},{"id":"https://openalex.org/keywords/positive-semidefinite-kernels","display_name":"positive semidefinite kernels","score":0.8730000257492065},{"id":"https://openalex.org/keywords/kernel-combination","display_name":"kernel combination","score":0.5680000185966492},{"id":"https://openalex.org/keywords/colon-cancer-classification","display_name":"colon cancer classification","score":0.6330000162124634}],"concepts":[{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.8345943093299866},{"id":"https://openalex.org/C181367576","wikidata":"https://www.wikidata.org/wiki/Q6394184","display_name":"Kernel Fisher discriminant analysis","level":4,"score":0.7149035334587097},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6587871313095093},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.6238057613372803},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5808977484703064},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.5640947222709656},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5446126461029053},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.5269977450370789},{"id":"https://openalex.org/C75866337","wikidata":"https://www.wikidata.org/wiki/Q7280263","display_name":"Radial basis function kernel","level":4,"score":0.5184704661369324},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49986863136291504},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.4731506109237671},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45706453919410706},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.45178329944610596},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.44196653366088867},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4342679977416992},{"id":"https://openalex.org/C207798031","wikidata":"https://www.wikidata.org/wiki/Q8563425","display_name":"Fisher kernel","level":5,"score":0.4219970107078552},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.42000243067741394},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.401020348072052},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.15708091855049133},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.06442111730575562},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1145/1015330.1015409","doi":"https://doi.org/10.1145/1015330.1015409","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1015330.1015409","pdf_url":null,"source":{"id":"https://openalex.org/S4306419644","display_name":"International Conference on Machine Learning","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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:10.1.1.2.2290","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.2.2290","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/264.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.4.9148","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.4.9148","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.wisc.edu/~gfung/icml_automatic_kernel1.ps","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.75.2776","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.75.2776","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.wisc.edu/~gfung/icml_automatic_kernel1.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.85.4294","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.85.4294","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.rpi.edu/~bij2/doc/icml04_kernel.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"display_name":"Reduced inequalities","score":0.75,"id":"https://metadata.un.org/sdg/10"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1520757484","https://openalex.org/W1528703258","https://openalex.org/W1553407966","https://openalex.org/W1563088657","https://openalex.org/W1589603734","https://openalex.org/W1596717185","https://openalex.org/W1601740268","https://openalex.org/W1770825568","https://openalex.org/W1946750680","https://openalex.org/W1979840843","https://openalex.org/W2052564542","https://openalex.org/W2064575768","https://openalex.org/W2069231281","https://openalex.org/W2093404847","https://openalex.org/W2110652811","https://openalex.org/W2128988020","https://openalex.org/W2132890143","https://openalex.org/W2135346934","https://openalex.org/W2145295623","https://openalex.org/W2150796457","https://openalex.org/W2156909104","https://openalex.org/W2521066952"],"related_works":["https://openalex.org/W2125231743","https://openalex.org/W2132890143","https://openalex.org/W2172295126","https://openalex.org/W1597291551","https://openalex.org/W1541405005","https://openalex.org/W1564016699","https://openalex.org/W7098983605","https://openalex.org/W2045325303","https://openalex.org/W4244156276","https://openalex.org/W2154220409"],"abstract_inverted_index":{"We":[0,125],"propose":[1],"a":[2,28,54,61,130],"fast":[3],"iterative":[4],"classification":[5],"algorithm":[6,74,102],"for":[7],"Kernel":[8],"Fisher":[9],"Discriminant":[10],"(KFD)":[11],"using":[12,122],"heterogeneous":[13],"kernel":[14,29,39,50,88,117],"models.":[15],"In":[16],"contrast":[17],"with":[18,79],"the":[19,24,33,41,82,87,100,111,116,137,140],"standard":[20,112],"KFD":[21,113],"that":[22,96,108,135],"requires":[23],"user":[25],"to":[26,44,60,81],"predefine":[27],"function,":[30],"we":[31],"incorporate":[32],"task":[34],"of":[35,49,57,65,72,84,99,139],"choosing":[36],"an":[37],"appropriate":[38],"into":[40],"optimization":[42],"problem":[43],"be":[45],"solved.":[46],"The":[47,70],"choice":[48],"is":[51,103],"defined":[52],"as":[53],"linear":[55],"combination":[56],"kernels":[58,85],"belonging":[59],"potentially":[62],"large":[63],"family":[64],"different":[66,106],"positive":[67],"semidefinite":[68],"kernels.":[69],"complexity":[71],"our":[73],"does":[75],"not":[76,104],"increase":[77],"significantly":[78,105],"respect":[80],"number":[83],"on":[86,91,129],"family.":[89],"Experiments":[90],"several":[92],"benchmark":[93],"datasets":[94],"demonstrate":[95,136],"generalization":[97],"performance":[98],"proposed":[101,141],"from":[107],"achieved":[109],"by":[110],"in":[114],"which":[115],"parameters":[118],"have":[119],"been":[120],"tuned":[121],"cross":[123],"validation.":[124],"also":[126],"present":[127],"results":[128],"real-life":[131],"colon":[132],"cancer":[133],"dataset":[134],"efficiency":[138],"method.":[142]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":2}],"updated_date":"2026-10-26T11:51:55.731654","created_date":"2016-06-24T00:00:00"}
