{"id":"https://openalex.org/W2166361453","doi":"https://doi.org/10.1109/tnn.2010.2064787","title":"Multiclass Relevance Vector Machines: Sparsity and Accuracy","display_name":"Multiclass Relevance Vector Machines: Sparsity and Accuracy","publication_year":2010,"publication_date":"2010-09-01","ids":{"openalex":"https://openalex.org/W2166361453","doi":"https://doi.org/10.1109/tnn.2010.2064787","mag":"2166361453","pmid":"https://pubmed.ncbi.nlm.nih.gov/20805053"},"language":"en","primary_location":{"id":"doi:10.1109/tnn.2010.2064787","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnn.2010.2064787","pdf_url":null,"source":{"id":"https://openalex.org/S42080949","display_name":"IEEE Transactions on Neural Networks","issn_l":"1045-9227","issn":["1045-9227","1941-0093"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5050662308","display_name":"Ioannis Psorakis","orcid":null},"institutions":[{"id":"https://openalex.org/I4210146410","display_name":"Science Oxford","ror":"https://ror.org/04j8yhy50","country_code":"GB","type":"nonprofit","lineage":["https://openalex.org/I4210146410"]},{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Ioannis Psorakis","raw_affiliation_strings":["Department of Engineering Science, University of Oxford, Oxford OX1 2JD, UK. yannis@robots.ox.ac.uk)","Department of Engineering Science, University of Oxford, Oxford, UK"],"affiliations":[{"raw_affiliation_string":"Department of Engineering Science, University of Oxford, Oxford OX1 2JD, UK. yannis@robots.ox.ac.uk)","institution_ids":["https://openalex.org/I4210146410","https://openalex.org/I40120149"]},{"raw_affiliation_string":"Department of Engineering Science, University of Oxford, Oxford, UK","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087781429","display_name":"Theodoros Damoulas","orcid":"https://orcid.org/0000-0002-7172-4829"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Theodoros Damoulas","raw_affiliation_strings":["Department of Computer Science, Cornell University, Ithaca, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045384249","display_name":"Mark Girolami","orcid":"https://orcid.org/0000-0003-3008-253X"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Mark A Girolami","raw_affiliation_strings":["Department of Statistical Science, University College London, London, UK"],"affiliations":[{"raw_affiliation_string":"Department of Statistical Science, University College London, London, UK","institution_ids":["https://openalex.org/I45129253"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5050662308"],"corresponding_institution_ids":["https://openalex.org/I40120149","https://openalex.org/I4210146410"],"apc_list":null,"apc_paid":null,"fwci":5.7491,"has_fulltext":false,"cited_by_count":144,"citation_normalized_percentile":{"value":0.9674203,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"21","issue":"10","first_page":"1588","last_page":"1598"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9958000183105469,"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"}},"topics":[{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9958000183105469,"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"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7622894644737244},{"id":"https://openalex.org/keywords/relevance-vector-machine","display_name":"Relevance vector machine","score":0.7436188459396362},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6980358362197876},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6936545372009277},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.6663587689399719},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6593432426452637},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5855724215507507},{"id":"https://openalex.org/keywords/multiclass-classification","display_name":"Multiclass classification","score":0.5575768947601318},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5180540084838867},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4756759703159332},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46793171763420105},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4402734935283661},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4191013276576996},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14593440294265747}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7622894644737244},{"id":"https://openalex.org/C14948415","wikidata":"https://www.wikidata.org/wiki/Q7310972","display_name":"Relevance vector machine","level":3,"score":0.7436188459396362},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6980358362197876},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6936545372009277},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.6663587689399719},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6593432426452637},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5855724215507507},{"id":"https://openalex.org/C123860398","wikidata":"https://www.wikidata.org/wiki/Q6934605","display_name":"Multiclass classification","level":3,"score":0.5575768947601318},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5180540084838867},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4756759703159332},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46793171763420105},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4402734935283661},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4191013276576996},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14593440294265747},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","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}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001185","descriptor_name":"Artificial Intelligence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001185","descriptor_name":"Artificial Intelligence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001185","descriptor_name":"Artificial Intelligence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D002965","descriptor_name":"Classification","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D002965","descriptor_name":"Classification","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D002965","descriptor_name":"Classification","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D016208","descriptor_name":"Databases, Factual","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016208","descriptor_name":"Databases, Factual","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016208","descriptor_name":"Databases, Factual","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D019295","descriptor_name":"Computational Biology","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D019295","descriptor_name":"Computational Biology","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D019295","descriptor_name":"Computational Biology","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false}],"locations_count":3,"locations":[{"id":"doi:10.1109/tnn.2010.2064787","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnn.2010.2064787","pdf_url":null,"source":{"id":"https://openalex.org/S42080949","display_name":"IEEE Transactions on Neural Networks","issn_l":"1045-9227","issn":["1045-9227","1941-0093"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks","raw_type":"journal-article"},{"id":"pmid:20805053","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/20805053","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks","raw_type":null},{"id":"pmh:oai:generic.eprints.org:1073224","is_oa":false,"landing_page_url":"http://publications.eng.cam.ac.uk/1073224/","pdf_url":null,"source":{"id":"https://openalex.org/S4406922847","display_name":"Cambridge University Engineering Department Publications Database","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.5899999737739563}],"awards":[{"id":"https://openalex.org/G6009955385","display_name":null,"funder_award_id":"EP/E052029/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W90656574","https://openalex.org/W1506806321","https://openalex.org/W1648445109","https://openalex.org/W1663973292","https://openalex.org/W1971402309","https://openalex.org/W1989175591","https://openalex.org/W1995945562","https://openalex.org/W2044696706","https://openalex.org/W2071956453","https://openalex.org/W2076723282","https://openalex.org/W2095692770","https://openalex.org/W2108306139","https://openalex.org/W2115606304","https://openalex.org/W2135046866","https://openalex.org/W2135763142","https://openalex.org/W2162382899","https://openalex.org/W2751318774","https://openalex.org/W2999905431","https://openalex.org/W3010521003","https://openalex.org/W4212863985","https://openalex.org/W4285719527","https://openalex.org/W6603656104"],"related_works":["https://openalex.org/W2066952721","https://openalex.org/W2565312173","https://openalex.org/W2335152656","https://openalex.org/W3147035969","https://openalex.org/W2359936972","https://openalex.org/W2039745824","https://openalex.org/W2383027800","https://openalex.org/W2206547991","https://openalex.org/W2104936869","https://openalex.org/W2186666570"],"abstract_inverted_index":{"In":[0,97],"this":[1,99],"paper,":[2],"we":[3,52],"investigate":[4],"the":[5,16,33,36,60,64,110],"sparsity":[6],"and":[7,67,81],"recognition":[8],"capabilities":[9],"of":[10,35,48,63,109],"two":[11],"approximate":[12],"Bayesian":[13],"classification":[14,71],"algorithms,":[15],"multiclass":[17,94],"multi-kernel":[18],"relevance":[19],"vector":[20],"machines":[21],"(mRVMs)":[22],"that":[23,58,87],"have":[24],"been":[25],"recently":[26],"proposed.":[27],"We":[28],"provide":[29],"an":[30],"insight":[31],"into":[32],"behavior":[34],"mRVM":[37],"models":[38],"by":[39,102],"performing":[40],"a":[41,45,105],"wide":[42],"experimentation":[43],"on":[44,93],"large":[46],"range":[47],"real-world":[49],"datasets.":[50],"Furthermore,":[51],"monitor":[53],"various":[54],"model":[55,82],"fitting":[56],"characteristics":[57],"identify":[59],"predictive":[61],"nature":[62],"proposed":[65],"methods":[66],"compare":[68],"against":[69],"existing":[70],"techniques.":[72],"By":[73],"introducing":[74],"novel":[75],"convergence":[76],"measures,":[77],"sample":[78],"selection":[79],"strategies":[80],"improvements,":[83],"it":[84],"is":[85,100],"demonstrated":[86],"mRVMs":[88],"can":[89],"produce":[90],"state-of-the-art":[91],"results":[92],"discrimination":[95],"problems.":[96],"addition,":[98],"achieved":[101],"utilizing":[103],"only":[104],"very":[106],"small":[107],"fraction":[108],"available":[111],"observation":[112],"data.":[113]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":13},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":15},{"year":2016,"cited_by_count":10},{"year":2015,"cited_by_count":18},{"year":2014,"cited_by_count":15},{"year":2013,"cited_by_count":7},{"year":2012,"cited_by_count":7}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
