{"id":"https://openalex.org/W2326390072","doi":"https://doi.org/10.1007/s10994-016-5562-z","title":"An aggregate and iterative disaggregate algorithm with proven optimality in machine learning","display_name":"An aggregate and iterative disaggregate algorithm with proven optimality in machine learning","publication_year":2016,"publication_date":"2016-03-22","ids":{"openalex":"https://openalex.org/W2326390072","doi":"https://doi.org/10.1007/s10994-016-5562-z","mag":"2326390072"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-016-5562-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-016-5562-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-016-5562-z.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-016-5562-z.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Young Woong Park","orcid":null},"institutions":[{"id":"https://openalex.org/I178169726","display_name":"Southern Methodist University","ror":"https://ror.org/042tdr378","country_code":"US","type":"education","lineage":["https://openalex.org/I178169726"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Young Woong Park","raw_affiliation_strings":["Cox School of Business, Southern Methodist University, Dallas, TX, USA"],"affiliations":[{"raw_affiliation_string":"Cox School of Business, Southern Methodist University, Dallas, TX, USA","institution_ids":["https://openalex.org/I178169726"]}]},{"author_position":"last","author":{"id":null,"display_name":"Diego Klabjan","orcid":null},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Diego Klabjan","raw_affiliation_strings":["Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA","institution_ids":["https://openalex.org/I111979921"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I178169726"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.01341854,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":"105","issue":"2","first_page":"199","last_page":"232"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.7347000241279602,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.7347000241279602,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.06480000168085098,"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":0.03909999877214432,"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/aggregate","display_name":"Aggregate (composite)","score":0.6965000033378601},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6890000104904175},{"id":"https://openalex.org/keywords/relevance-vector-machine","display_name":"Relevance vector machine","score":0.4668000042438507},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4302000105381012},{"id":"https://openalex.org/keywords/iterative-method","display_name":"Iterative method","score":0.4178999960422516},{"id":"https://openalex.org/keywords/computational-learning-theory","display_name":"Computational learning theory","score":0.41679999232292175},{"id":"https://openalex.org/keywords/online-machine-learning","display_name":"Online machine learning","score":0.4036000072956085},{"id":"https://openalex.org/keywords/least-absolute-deviations","display_name":"Least absolute deviations","score":0.33899998664855957}],"concepts":[{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.6965000033378601},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6890000104904175},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5705999732017517},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5473999977111816},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.544700026512146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.505299985408783},{"id":"https://openalex.org/C14948415","wikidata":"https://www.wikidata.org/wiki/Q7310972","display_name":"Relevance vector machine","level":3,"score":0.4668000042438507},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4519999921321869},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4302000105381012},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.4178999960422516},{"id":"https://openalex.org/C50292564","wikidata":"https://www.wikidata.org/wiki/Q2462783","display_name":"Computational learning theory","level":3,"score":0.41679999232292175},{"id":"https://openalex.org/C115903097","wikidata":"https://www.wikidata.org/wiki/Q7094097","display_name":"Online machine learning","level":3,"score":0.4036000072956085},{"id":"https://openalex.org/C31441030","wikidata":"https://www.wikidata.org/wiki/Q4291882","display_name":"Least absolute deviations","level":3,"score":0.33899998664855957},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3280999958515167},{"id":"https://openalex.org/C2987595161","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Optimization algorithm","level":2,"score":0.31769999861717224},{"id":"https://openalex.org/C120934525","wikidata":"https://www.wikidata.org/wiki/Q849149","display_name":"Absolute deviation","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C117619785","wikidata":"https://www.wikidata.org/wiki/Q6094414","display_name":"Iterative learning control","level":3,"score":0.2759999930858612},{"id":"https://openalex.org/C2778207910","wikidata":"https://www.wikidata.org/wiki/Q397610","display_name":"Agr\u00e9gation","level":3,"score":0.2662999927997589},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C184497298","wikidata":"https://www.wikidata.org/wiki/Q7229773","display_name":"Population-based incremental learning","level":3,"score":0.2522999942302704}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10994-016-5562-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-016-5562-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-016-5562-z.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1607.01400","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1607.01400","pdf_url":"https://arxiv.org/pdf/1607.01400","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1007/s10994-016-5562-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-016-5562-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-016-5562-z.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2326390072.pdf","grobid_xml":"https://content.openalex.org/works/W2326390072.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1489608533","https://openalex.org/W1529313332","https://openalex.org/W1648550497","https://openalex.org/W1876253797","https://openalex.org/W1965059296","https://openalex.org/W1997346006","https://openalex.org/W2007786724","https://openalex.org/W2014158063","https://openalex.org/W2019288156","https://openalex.org/W2047953381","https://openalex.org/W2069258770","https://openalex.org/W2076080604","https://openalex.org/W2095487789","https://openalex.org/W2095897464","https://openalex.org/W2117708047","https://openalex.org/W2122565017","https://openalex.org/W2140095548","https://openalex.org/W2147610175","https://openalex.org/W2153635508","https://openalex.org/W2153686651","https://openalex.org/W2158624777","https://openalex.org/W2314893179","https://openalex.org/W2582743722","https://openalex.org/W4213065242","https://openalex.org/W6628746421","https://openalex.org/W6675354045","https://openalex.org/W6676348322","https://openalex.org/W6677656871"],"related_works":[],"abstract_inverted_index":null,"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2016-06-24T00:00:00"}
