{"id":"https://openalex.org/W7066013632","doi":null,"title":"Fast Multiple Instance Learning Via L1,2 Logistic Regression","display_name":"Fast Multiple Instance Learning Via L1,2 Logistic Regression","publication_year":2012,"publication_date":"2012-03-10","ids":{"openalex":"https://openalex.org/W7066013632"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.214.4991","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.214.4991","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://figment.cse.usf.edu/~sfefilat/data/papers/ThBCT8.50.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5112557825","display_name":"Zhouyu Fu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhouyu Fu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5010621788","display_name":"Antonio Robles\u2010Kelly","orcid":"https://orcid.org/0000-0002-2465-5971","observed_orcids":["https://orcid.org/0000-0002-2465-5971"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Antonio Robles-kelly","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.20919999480247498,"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/T10136","display_name":"Statistical Methods and Inference","score":0.20919999480247498,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.12960000336170197,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.11069999635219574,"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/multiple-instance-learning","display_name":"multiple instance learning","score":0.9990000128746033},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"logistic regression","score":0.8790000081062317},{"id":"https://openalex.org/keywords/l1-regularization","display_name":"L1 regularization","score":0.925000011920929},{"id":"https://openalex.org/keywords/tikhonov-regularization","display_name":"Tikhonov regularization","score":0.9990000128746033},{"id":"https://openalex.org/keywords/sparse-feature-selection","display_name":"sparse feature selection","score":0.675000011920929},{"id":"https://openalex.org/keywords/quasi-newton-methods","display_name":"quasi-Newton methods","score":0.9290000200271606},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.5730000138282776}],"concepts":[{"id":"https://openalex.org/C61722155","wikidata":"https://www.wikidata.org/wiki/Q6667643","display_name":"Logistic model tree","level":3,"score":0.8273000121116638},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.8172000050544739},{"id":"https://openalex.org/C117568660","wikidata":"https://www.wikidata.org/wiki/Q1650843","display_name":"Multinomial logistic regression","level":2,"score":0.6053000092506409},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5105999708175659},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5009999871253967},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4447000026702881},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4334000051021576},{"id":"https://openalex.org/C180802074","wikidata":"https://www.wikidata.org/wiki/Q1052379","display_name":"Logistic function","level":2,"score":0.4115000069141388},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.38100001215934753},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36739999055862427},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C94465730","wikidata":"https://www.wikidata.org/wiki/Q589603","display_name":"Logistic distribution","level":3,"score":0.3127000033855438},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3095000088214874},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.29670000076293945},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C60316415","wikidata":"https://www.wikidata.org/wiki/Q6664520","display_name":"Local regression","level":4,"score":0.2567000091075897},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.214.4991","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.214.4991","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://figment.cse.usf.edu/~sfefilat/data/papers/ThBCT8.50.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3003935205","https://openalex.org/W4411161293","https://openalex.org/W2138145347","https://openalex.org/W3022338767","https://openalex.org/W7098392619","https://openalex.org/W2118697091","https://openalex.org/W2243646441","https://openalex.org/W2963484268","https://openalex.org/W138876763","https://openalex.org/W1169446106"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,45],"develop":[4],"an":[5,68],"efficient":[6],"logistic":[7,24,71,87],"regression":[8,25,72],"model":[9,26,43,73],"for":[10],"multiple":[11],"instance":[12],"learning":[13],"that":[14],"combines":[15],"L1":[16,22,42,53,86],"and":[17,94],"L2":[18,69],"regularisation":[19],"techniques.":[20],"An":[21],"regularised":[23,70],"is":[27],"first":[28],"learned":[29],"to":[30,101],"find":[31],"out":[32],"the":[33,37,41,52,76,85,92,97],"sparse":[34],"pattern":[35],"of":[36,51,78,96,104],"features.":[38],"To":[39],"train":[40,67],"efficiently,":[44],"employ":[46],"a":[47,61,102],"convex":[48],"differentiable":[49],"approximation":[50],"cost":[54],"function":[55],"which":[56],"can":[57],"be":[58],"solved":[59],"by":[60,84],"quasi":[62],"Newton":[63],"method.":[64],"We":[65],"then":[66],"only":[74],"on":[75],"subset":[77],"features":[79],"with":[80],"nonzero":[81],"weights":[82],"returned":[83],"regression.":[88],"Experimental":[89],"results":[90],"demonstrate":[91],"utility":[93],"efficiency":[95],"proposed":[98],"approach":[99],"compared":[100],"number":[103],"alternatives.":[105],"1.":[106]},"counts_by_year":[{"year":2017,"cited_by_count":2}],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2025-10-10T00:00:00"}
