{"id":"https://openalex.org/W2147533695","doi":"https://doi.org/10.1109/cvpr.2010.5540231","title":"P-N learning: Bootstrapping binary classifiers by structural constraints","display_name":"P-N learning: Bootstrapping binary classifiers by structural constraints","publication_year":2010,"publication_date":"2010-06-01","ids":{"openalex":"https://openalex.org/W2147533695","doi":"https://doi.org/10.1109/cvpr.2010.5540231","mag":"2147533695"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2010.5540231","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2010.5540231","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://dspace.cvut.cz/bitstream/10467/9552/1/2010-P-n-learning-Bootstrapping-binary-classifiers-by-structural-constraints.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000594829","display_name":"Zdenek Kalal","orcid":null},"institutions":[{"id":"https://openalex.org/I28290843","display_name":"University of Surrey","ror":"https://ror.org/00ks66431","country_code":"GB","type":"education","lineage":["https://openalex.org/I28290843"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Zdenek Kalal","raw_affiliation_strings":["University of Surrey, Guildford, UK"],"affiliations":[{"raw_affiliation_string":"University of Surrey, Guildford, UK","institution_ids":["https://openalex.org/I28290843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007656938","display_name":"Ji\u0159\u0131\u0301 Matas","orcid":"https://orcid.org/0000-0003-0863-4844"},"institutions":[{"id":"https://openalex.org/I44504214","display_name":"Czech Technical University in Prague","ror":"https://ror.org/03kqpb082","country_code":"CZ","type":"education","lineage":["https://openalex.org/I44504214"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Jiri Matas","raw_affiliation_strings":["Czech Technical University, Prague, Czech Republic"],"affiliations":[{"raw_affiliation_string":"Czech Technical University, Prague, Czech Republic","institution_ids":["https://openalex.org/I44504214"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024769212","display_name":"Krystian Mikolajczyk","orcid":"https://orcid.org/0000-0003-0726-9187"},"institutions":[{"id":"https://openalex.org/I28290843","display_name":"University of Surrey","ror":"https://ror.org/00ks66431","country_code":"GB","type":"education","lineage":["https://openalex.org/I28290843"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Krystian Mikolajczyk","raw_affiliation_strings":["University of Surrey, Guildford, UK"],"affiliations":[{"raw_affiliation_string":"University of Surrey, Guildford, UK","institution_ids":["https://openalex.org/I28290843"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5000594829"],"corresponding_institution_ids":["https://openalex.org/I28290843"],"apc_list":null,"apc_paid":null,"fwci":122.4226,"has_fulltext":true,"cited_by_count":1025,"citation_normalized_percentile":{"value":0.99979695,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"49","last_page":"56"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9987000226974487,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9987000226974487,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.998199999332428,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9980000257492065,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7563226222991943},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7513402104377747},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7213089466094971},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5871626138687134},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5504072308540344},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.5408108830451965},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4966462254524231},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.4465065002441406},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4372027516365051},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.43436312675476074},{"id":"https://openalex.org/keywords/learning-classifier-system","display_name":"Learning classifier system","score":0.4237772822380066},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.3075289726257324},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16331544518470764}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7563226222991943},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7513402104377747},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7213089466094971},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5871626138687134},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5504072308540344},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5408108830451965},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4966462254524231},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.4465065002441406},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4372027516365051},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.43436312675476074},{"id":"https://openalex.org/C199190896","wikidata":"https://www.wikidata.org/wiki/Q3509276","display_name":"Learning classifier system","level":3,"score":0.4237772822380066},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.3075289726257324},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16331544518470764},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/cvpr.2010.5540231","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2010.5540231","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:alma.44SUR_INST:11140143150002346","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4210197018","display_name":"View","issn_l":"2688-268X","issn":["2688-268X","2688-3988"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"","raw_type":""},{"id":"pmh:oai:dspace.cvut.cz:10467/9552","is_oa":true,"landing_page_url":"http://hdl.handle.net/10467/9552","pdf_url":"https://dspace.cvut.cz/bitstream/10467/9552/1/2010-P-n-learning-Bootstrapping-binary-classifiers-by-structural-constraints.pdf","source":{"id":"https://openalex.org/S4306400739","display_name":"Cvut DSpace (Czech Technical University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I44504214","host_organization_name":"Czech Technical University in Prague","host_organization_lineage":["https://openalex.org/I44504214"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"p\u0159\u00edsp\u011bvek z konference - elektronick\u00fd"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.231.4328","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.231.4328","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://info.ee.surrey.ac.uk/Personal/Z.Kalal/Publications/2010_cvpr.pdf","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:dspace.cvut.cz:10467/9552","is_oa":true,"landing_page_url":"http://hdl.handle.net/10467/9552","pdf_url":"https://dspace.cvut.cz/bitstream/10467/9552/1/2010-P-n-learning-Bootstrapping-binary-classifiers-by-structural-constraints.pdf","source":{"id":"https://openalex.org/S4306400739","display_name":"Cvut DSpace (Czech Technical University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I44504214","host_organization_name":"Czech Technical University in Prague","host_organization_lineage":["https://openalex.org/I44504214"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"p\u0159\u00edsp\u011bvek z konference - elektronick\u00fd"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321006","display_name":"Grantov\u00e1 Agentura \u010cesk\u00e9 Republiky","ror":"https://ror.org/01pv73b02"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2147533695.pdf","grobid_xml":"https://content.openalex.org/works/W2147533695.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1479807131","https://openalex.org/W1868559974","https://openalex.org/W1980547439","https://openalex.org/W1990334093","https://openalex.org/W2048679005","https://openalex.org/W2065313971","https://openalex.org/W2079057609","https://openalex.org/W2086274040","https://openalex.org/W2097089247","https://openalex.org/W2097860170","https://openalex.org/W2101886560","https://openalex.org/W2110671801","https://openalex.org/W2115209694","https://openalex.org/W2118877769","https://openalex.org/W2121680631","https://openalex.org/W2131296063","https://openalex.org/W2150000644","https://openalex.org/W2153927146","https://openalex.org/W2154422044","https://openalex.org/W2161160446","https://openalex.org/W2164598857","https://openalex.org/W2167089254","https://openalex.org/W2170865122","https://openalex.org/W2293873019","https://openalex.org/W2911964244","https://openalex.org/W3195133498","https://openalex.org/W6676243939","https://openalex.org/W6677204999","https://openalex.org/W6677548441","https://openalex.org/W6683794642","https://openalex.org/W6684274140"],"related_works":["https://openalex.org/W3148060700","https://openalex.org/W34092691","https://openalex.org/W2365028544","https://openalex.org/W4309984931","https://openalex.org/W2949671220","https://openalex.org/W2794908468","https://openalex.org/W2096363773","https://openalex.org/W2531570999","https://openalex.org/W4310801741","https://openalex.org/W3210156800"],"abstract_inverted_index":{"This":[0],"paper":[1],"shows":[2],"that":[3,53,89,115,154],"the":[4,15,27,33,36,72,75,81,84,100,104,117,126,142,172],"performance":[5],"of":[6,17,29,35,74,125,144,147,190],"a":[7,40,45,113,163,188],"binary":[8,46],"classifier":[9,47,82,128],"can":[10,159],"be":[11,160],"significantly":[12],"improved":[13],"by":[14,63],"processing":[16],"structured":[18,24],"unlabeled":[19,51,76,85,168],"data,":[20,86],"i.e.":[21],"data":[22],"are":[23],"if":[25],"knowing":[26],"label":[28],"one":[30],"example":[31,165],"restricts":[32],"labeling":[34,73],"others.":[37],"We":[38,111,152],"propose":[39,112],"novel":[41],"paradigm":[42],"for":[43],"training":[44,101],"from":[48,162],"labeled":[49],"and":[50,66,98,129,134,166,183,196],"examples":[52,88],"we":[54],"call":[55],"P-N":[56,78,121,137],"learning.":[57],"The":[58,176],"learning":[59,79,122,138,146],"process":[60],"is":[61,139,178,185],"guided":[62],"positive":[64],"(P)":[65],"negative":[67],"(N)":[68],"constraints":[69,97],"which":[70,120],"restrict":[71],"set.":[77],"evaluates":[80],"on":[83,132,187],"identifies":[87],"have":[90],"been":[91],"classified":[92],"in":[93,107],"contradiction":[94],"with":[95,103,180],"structural":[96],"augments":[99],"set":[102],"corrected":[105],"samples":[106],"an":[108,155,167],"iterative":[109],"process.":[110],"theory":[114],"formulates":[116],"conditions":[118],"under":[119],"guarantees":[123],"improvement":[124],"initial":[127],"validate":[130],"it":[131],"synthetic":[133],"real":[135],"data.":[136],"applied":[140],"to":[141],"problem":[143],"on-line":[145],"object":[148,157,173],"detector":[149,158],"during":[150],"tracking.":[151],"show":[153],"accurate":[156],"learned":[161],"single":[164],"video":[169],"sequence":[170],"where":[171],"may":[174],"occur.":[175],"algorithm":[177],"compared":[179],"related":[181],"approaches":[182],"state-of-the-art":[184],"achieved":[186],"variety":[189],"objects":[191],"(faces,":[192],"pedestrians,":[193],"cars,":[194],"motorbikes":[195],"animals).":[197]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":25},{"year":2020,"cited_by_count":35},{"year":2019,"cited_by_count":50},{"year":2018,"cited_by_count":75},{"year":2017,"cited_by_count":122},{"year":2016,"cited_by_count":131},{"year":2015,"cited_by_count":160},{"year":2014,"cited_by_count":141},{"year":2013,"cited_by_count":147},{"year":2012,"cited_by_count":84}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
