{"id":"https://openalex.org/W2808856927","doi":"https://doi.org/10.1145/3219819.3219967","title":"Learning Dynamics of Decision Boundaries without Additional Labeled Data","display_name":"Learning Dynamics of Decision Boundaries without Additional Labeled Data","publication_year":2018,"publication_date":"2018-07-19","ids":{"openalex":"https://openalex.org/W2808856927","doi":"https://doi.org/10.1145/3219819.3219967","mag":"2808856927"},"language":"en","primary_location":{"id":"doi:10.1145/3219819.3219967","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3219819.3219967","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5030880294","display_name":"Atsutoshi Kumagai","orcid":"https://orcid.org/0000-0002-2915-4615"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Atsutoshi Kumagai","raw_affiliation_strings":["NTT Secure Platform Laboratories, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Secure Platform Laboratories, Tokyo, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034538103","display_name":"Tomoharu Iwata","orcid":"https://orcid.org/0000-0003-4425-1971"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomoharu Iwata","raw_affiliation_strings":["NTT Communication Science Laboratories, Kyoto, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2251713219"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1627","last_page":"1636"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9994000196456909,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9994000196456909,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9977999925613403,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/decision-boundary","display_name":"Decision boundary","score":0.7320196032524109},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6844214200973511},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5857101082801819},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5679070949554443},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5112056732177734},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.506841242313385},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.505549430847168},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5000424385070801},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.48776036500930786},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.48550963401794434},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4492715895175934},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.4345988631248474},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4130449593067169},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4014959931373596},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4006410539150238},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3832983672618866},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3712849020957947},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22824254631996155}],"concepts":[{"id":"https://openalex.org/C42023084","wikidata":"https://www.wikidata.org/wiki/Q5249231","display_name":"Decision boundary","level":3,"score":0.7320196032524109},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6844214200973511},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5857101082801819},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5679070949554443},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5112056732177734},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.506841242313385},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.505549430847168},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5000424385070801},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.48776036500930786},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.48550963401794434},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4492715895175934},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.4345988631248474},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4130449593067169},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4014959931373596},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4006410539150238},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3832983672618866},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3712849020957947},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22824254631996155},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3219819.3219967","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3219819.3219967","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7799999713897705,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W43161092","https://openalex.org/W134197611","https://openalex.org/W1519626139","https://openalex.org/W1582340466","https://openalex.org/W1603138887","https://openalex.org/W1663973292","https://openalex.org/W1966771059","https://openalex.org/W2001474264","https://openalex.org/W2005295545","https://openalex.org/W2009727399","https://openalex.org/W2031214791","https://openalex.org/W2034368206","https://openalex.org/W2082772936","https://openalex.org/W2097197066","https://openalex.org/W2099419573","https://openalex.org/W2104290444","https://openalex.org/W2105934661","https://openalex.org/W2112076978","https://openalex.org/W2115403315","https://openalex.org/W2121990650","https://openalex.org/W2129767422","https://openalex.org/W2145494108","https://openalex.org/W2158559550","https://openalex.org/W2165698076","https://openalex.org/W2170935389","https://openalex.org/W2171809276","https://openalex.org/W2218835453","https://openalex.org/W2252617635","https://openalex.org/W2334687549","https://openalex.org/W2465325663","https://openalex.org/W2470412537","https://openalex.org/W2566637231","https://openalex.org/W2604345102","https://openalex.org/W2626498001","https://openalex.org/W2737492962","https://openalex.org/W2741731870","https://openalex.org/W2765101016","https://openalex.org/W2950361018","https://openalex.org/W2963641944","https://openalex.org/W2964040467","https://openalex.org/W2964278684","https://openalex.org/W2997701990","https://openalex.org/W3003824022","https://openalex.org/W3005522790","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W1982461258","https://openalex.org/W2990563994","https://openalex.org/W365792495","https://openalex.org/W2027784570","https://openalex.org/W2045191946","https://openalex.org/W2012352575","https://openalex.org/W2049881039","https://openalex.org/W577779381","https://openalex.org/W3076662078","https://openalex.org/W4214574858"],"abstract_inverted_index":{"We":[0,136,169],"propose":[1],"a":[2,144],"method":[3,62,188],"for":[4,175],"learning":[5],"the":[6,9,27,35,42,87,90,93,105,111,116,133,149,154,157,161,164,176,186],"dynamics":[7,91],"of":[8,37,92,156,163,185],"decision":[10,28,94,106,117],"boundary":[11,29,95,118],"to":[12,77,153],"maintain":[13],"classification":[14],"performance":[15,36,67],"without":[16],"additional":[17,47],"labeled":[18,48,82],"data.":[19,49],"In":[20],"various":[21],"applications,":[22],"such":[23,53],"as":[24,79,81],"spam-mail":[25],"classification,":[26],"dynamically":[30],"changes":[31],"over":[32],"time.":[33],"Accordingly,":[34],"classifiers":[38,43],"deteriorates":[39],"quickly":[40],"unless":[41],"are":[44,75],"retrained":[45],"using":[46,69,193],"However,":[50],"continuously":[51],"preparing":[52],"data":[54,83,199],"is":[55,96,130],"quite":[56],"expensive":[57],"or":[58],"impossible.":[59],"The":[60,183],"proposed":[61,88,134,187],"alleviates":[63],"this":[64,138],"deterioration":[65],"in":[66,127,143],"by":[68,98,147],"newly":[70],"obtained":[71],"unlabeled":[72,109],"data,":[73,110],"which":[74],"easy":[76],"prepare,":[78],"well":[80],"collected":[84],"beforehand.":[85],"With":[86],"method,":[89],"modeled":[97],"Gaussian":[99],"processes.":[100],"To":[101],"exploit":[102],"information":[103],"on":[104,160,179],"boundaries":[107],"from":[108],"low-density":[112,128],"separation":[113],"criterion,":[114],"i.e.,":[115],"should":[119],"not":[120],"cross":[121],"high-density":[122],"regions,":[123,129],"but":[124],"instead":[125],"lie":[126],"assumed":[131],"with":[132],"method.":[135],"incorporate":[137],"criterion":[139],"into":[140],"our":[141],"framework":[142],"principled":[145],"manner":[146],"introducing":[148],"entropy":[150],"posterior":[151,155],"regularization":[152],"classifier":[158],"parameters":[159],"basis":[162],"generic":[165],"regularized":[166],"Bayesian":[167,181],"framework.":[168],"developed":[170],"an":[171],"efficient":[172],"inference":[173],"algorithm":[174],"model":[177],"based":[178],"variational":[180],"inference.":[182],"effectiveness":[184],"was":[189],"demonstrated":[190],"through":[191],"experiments":[192],"two":[194],"synthetic":[195],"and":[196],"four":[197],"real-world":[198],"sets.":[200]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
