{"id":"https://openalex.org/W1967170228","doi":"https://doi.org/10.1109/icmla.2013.84","title":"Deep Multiple Kernel Learning","display_name":"Deep Multiple Kernel Learning","publication_year":2013,"publication_date":"2013-12-01","ids":{"openalex":"https://openalex.org/W1967170228","doi":"https://doi.org/10.1109/icmla.2013.84","mag":"1967170228"},"language":"en","primary_location":{"id":"doi:10.1109/icmla.2013.84","doi":"https://doi.org/10.1109/icmla.2013.84","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla.2013.84","pdf_url":null,"source":{"id":"https://openalex.org/S4306419645","display_name":"International Conference on Machine Learning and Applications","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":"2013 12th International Conference on Machine Learning and Applications","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1310.3101","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001887367","display_name":"Eric V. Strobl","orcid":"https://orcid.org/0000-0002-0077-6314","observed_orcids":["https://orcid.org/0000-0002-0077-6314","https://orcid.org/0009-0003-9894-9694"]},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eric V. Strobl","raw_affiliation_strings":["Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA","Dept. of Biomed. Inf., Univ. of Pittsburgh, Pittsburgh, PA, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA","institution_ids":["https://openalex.org/I170201317"]},{"raw_affiliation_string":"Dept. of Biomed. Inf., Univ. of Pittsburgh, Pittsburgh, PA, USA#TAB#","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036362467","display_name":"Shyam Visweswaran","orcid":"https://orcid.org/0000-0002-2079-8684","observed_orcids":["https://orcid.org/0000-0002-2079-8684"]},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shyam Visweswaran","raw_affiliation_strings":["Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA","Dept. of Biomed. Inf., Univ. of Pittsburgh, Pittsburgh, PA, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA","institution_ids":["https://openalex.org/I170201317"]},{"raw_affiliation_string":"Dept. of Biomed. Inf., Univ. of Pittsburgh, Pittsburgh, PA, USA#TAB#","institution_ids":["https://openalex.org/I170201317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170201317"],"apc_list":null,"apc_paid":null,"fwci":2.0395,"has_fulltext":true,"cited_by_count":57,"citation_normalized_percentile":{"value":0.87768804,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"414","last_page":"417"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.5512999892234802,"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/T12676","display_name":"Machine Learning and ELM","score":0.5512999892234802,"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":0.16609999537467957,"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":0.09220000356435776,"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-learning","display_name":"kernel learning","score":0.6119999885559082},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.6639999747276306},{"id":"https://openalex.org/keywords/leave-one-out-error","display_name":"leave-one-out error","score":0.8989999890327454},{"id":"https://openalex.org/keywords/kernel-combination","display_name":"kernel combination","score":0.6299999952316284},{"id":"https://openalex.org/keywords/small-sample-size","display_name":"small sample size","score":0.5230000019073486}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7708408236503601},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.7229113578796387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7146723866462708},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.61457359790802},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5868255496025085},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5857173204421997},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5535041093826294},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5523227453231812},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5142917037010193},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.4923321008682251},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.46010836958885193},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33258724212646484},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.142675518989563},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icmla.2013.84","doi":"https://doi.org/10.1109/icmla.2013.84","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla.2013.84","pdf_url":null,"source":{"id":"https://openalex.org/S4306419645","display_name":"International Conference on Machine Learning and Applications","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":"2013 12th International Conference on Machine Learning and Applications","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1310.3101","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1310.3101","pdf_url":"https://arxiv.org/pdf/1310.3101","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.1310.3101","doi":"https://doi.org/10.48550/arxiv.1310.3101","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1310.3101","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1310.3101","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1310.3101","pdf_url":"https://arxiv.org/pdf/1310.3101","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G3332827520","display_name":"Medical Scientist Training Program","funder_award_id":"T32GM008208","funder_id":"https://openalex.org/F4320337354","funder_display_name":"National Institute of General Medical Sciences"}],"funders":[{"id":"https://openalex.org/F4320310174","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337354","display_name":"National Institute of General Medical Sciences","ror":"https://ror.org/04q48ey07"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W1967170228.pdf","grobid_xml":"https://content.openalex.org/works/W1967170228.grobid-xml"},"referenced_works_count":6,"referenced_works":["https://openalex.org/W1559159361","https://openalex.org/W2065897648","https://openalex.org/W2115390216","https://openalex.org/W2137055149","https://openalex.org/W2158001550","https://openalex.org/W2167608136"],"related_works":["https://openalex.org/W1945125930","https://openalex.org/W2551683770","https://openalex.org/W2137055149","https://openalex.org/W3154707168","https://openalex.org/W2505720795","https://openalex.org/W3134074277","https://openalex.org/W3036761806","https://openalex.org/W2167608136","https://openalex.org/W2939459869","https://openalex.org/W2785433750"],"abstract_inverted_index":{"Deep":[0],"learning":[1,39],"methods":[2],"have":[3],"predominantly":[4],"been":[5],"applied":[6],"to":[7,24],"large":[8,17],"artificial":[9],"neural":[10],"networks.":[11],"Despite":[12],"their":[13],"state-of-the-art":[14],"performance,":[15],"these":[16],"networks":[18],"typically":[19],"do":[20],"not":[21],"generalize":[22],"well":[23],"datasets":[25,75],"with":[26,83],"limited":[27],"sample":[28],"sizes.":[29],"In":[30],"this":[31],"paper,":[32],"we":[33],"take":[34],"a":[35,72,85],"different":[36],"approach":[37],"by":[38],"multiple":[40],"layers":[41],"of":[42,56,74],"kernels.":[43,88],"We":[44],"combine":[45],"kernels":[46],"at":[47],"each":[48,78],"layer":[49,79],"and":[50],"then":[51],"optimize":[52],"over":[53],"an":[54],"estimate":[55],"the":[57,65],"support":[58],"vector":[59],"machine":[60],"leave-one-out":[61],"error":[62],"rather":[63],"than":[64],"dual":[66],"objective":[67],"function.":[68],"Our":[69],"experiments":[70],"on":[71],"variety":[73],"show":[76],"that":[77],"successively":[80],"increases":[81],"performance":[82],"only":[84],"few":[86],"base":[87]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":9},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":1}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2016-06-24T00:00:00"}
