{"id":"https://openalex.org/W2146571341","doi":"https://doi.org/10.1109/tpami.2003.1227989","title":"Adaptive sparseness for supervised learning","display_name":"Adaptive sparseness for supervised learning","publication_year":2003,"publication_date":"2003-09-01","ids":{"openalex":"https://openalex.org/W2146571341","doi":"https://doi.org/10.1109/tpami.2003.1227989","mag":"2146571341"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2003.1227989","doi":"https://doi.org/10.1109/tpami.2003.1227989","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2003.1227989","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["ccf-a","cwts-core","dongbi-a","doyens","jufo-3","medline","norway-2","poland-200","russia-white-list-1"],"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"parent_works":[],"lineage":["https://openalex.org/W2146571341"],"child_works_count":0,"type":"article","is_traditional":true,"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/A5026826555","display_name":"M\u00e1rio A. T. Figueiredo","orcid":"https://orcid.org/0000-0002-0970-7745","observed_orcids":["https://orcid.org/0000-0002-0970-7745"]},"institutions":[{"id":"https://openalex.org/I4387152517","display_name":"Instituto Superior T\u00e9cnico","ror":"https://ror.org/03db2by73","country_code":"PT","type":"education","lineage":["https://openalex.org/I141596103","https://openalex.org/I4387152517"]}],"countries":["PT"],"is_corresponding":true,"raw_author_name":"M.A.T. Figueiredo","raw_affiliation_strings":["Institute of Telecommunications and the Department of Electrical and Computer Engineering, Instituto Superior T\u00e9cnico, Lisboa, Portugal","Dept. of Electr. & Comput. Eng., Inst. Superior Tecnico, Lisboa, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Telecommunications and the Department of Electrical and Computer Engineering, Instituto Superior T\u00e9cnico, Lisboa, Portugal","institution_ids":["https://openalex.org/I4387152517"]},{"raw_affiliation_string":"Dept. of Electr. & Comput. Eng., Inst. Superior Tecnico, Lisboa, Portugal","institution_ids":["https://openalex.org/I4387152517"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5026826555"],"corresponding_institution_ids":["https://openalex.org/I4387152517"],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":13.2094,"has_fulltext":false,"cited_by_count":533,"citation_normalized_percentile":{"value":0.98961102,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"25","issue":"9","first_page":"1150","last_page":"1159"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.384799987077713,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.384799987077713,"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/T10136","display_name":"Statistical Methods and Inference","score":0.14090000092983246,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.0697999969124794,"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/sparse-classifiers","display_name":"sparse classifiers","score":0.7770000100135803},{"id":"https://openalex.org/keywords/laplacian-prior","display_name":"Laplacian prior","score":0.9649999737739563},{"id":"https://openalex.org/keywords/expectation-maximization-algorithm","display_name":"expectation-maximization algorithm","score":0.7710000276565552},{"id":"https://openalex.org/keywords/model-complexity-control","display_name":"model complexity control","score":0.6179999709129333},{"id":"https://openalex.org/keywords/support-vector-machine-comparison","display_name":"support vector machine comparison","score":0.7200000286102295}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7299224734306335},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6688107252120972},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.636340320110321},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6127562522888184},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5962235331535339},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5104309916496277},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.47923973202705383},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.44741031527519226},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42863696813583374},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2646787762641907},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2003.1227989","doi":"https://doi.org/10.1109/tpami.2003.1227989","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2003.1227989","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["ccf-a","cwts-core","dongbi-a","doyens","jufo-3","medline","norway-2","poland-200","russia-white-list-1"],"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.5.3864","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.5.3864","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.lx.it.pt/%7Emtf/IEEE_TPAMI_2003.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W617853731","https://openalex.org/W1528905581","https://openalex.org/W1563088657","https://openalex.org/W1567512734","https://openalex.org/W1581001755","https://openalex.org/W1594031697","https://openalex.org/W1657213141","https://openalex.org/W1965026065","https://openalex.org/W1986931325","https://openalex.org/W1988520084","https://openalex.org/W2011873184","https://openalex.org/W2047028564","https://openalex.org/W2062102668","https://openalex.org/W2067633806","https://openalex.org/W2078204800","https://openalex.org/W2107710506","https://openalex.org/W2108306139","https://openalex.org/W2112545207","https://openalex.org/W2117063635","https://openalex.org/W2117812871","https://openalex.org/W2124335859","https://openalex.org/W2125781844","https://openalex.org/W2129869373","https://openalex.org/W2135046866","https://openalex.org/W2135338737","https://openalex.org/W2137512539","https://openalex.org/W2143956139","https://openalex.org/W2146766088","https://openalex.org/W2148603752","https://openalex.org/W2150962169","https://openalex.org/W2156706175","https://openalex.org/W2156909104","https://openalex.org/W2157423413","https://openalex.org/W2158940042","https://openalex.org/W2170780515","https://openalex.org/W3015463134","https://openalex.org/W3085162807","https://openalex.org/W4234698323","https://openalex.org/W4251699153","https://openalex.org/W4298876635","https://openalex.org/W4301861531","https://openalex.org/W6676234565"],"related_works":["https://openalex.org/W2125781844","https://openalex.org/W2150962169","https://openalex.org/W4281716507","https://openalex.org/W7028167965","https://openalex.org/W2171606554","https://openalex.org/W2159611475","https://openalex.org/W7097811151","https://openalex.org/W7101063781","https://openalex.org/W7100127491","https://openalex.org/W2041922787"],"abstract_inverted_index":{"The":[0],"goal":[1],"of":[2,15,29,46,95,97,133,144,193],"supervised":[3,57],"learning":[4],"is":[5,23,37,127,138,150],"to":[6,25,56,61,74,78,104,121],"infer":[7],"a":[8,13,41,53,130,145],"functional":[9],"mapping":[10],"based":[11],"on":[12],"set":[14,72],"training":[16,110],"examples.":[17],"To":[18],"achieve":[19],"good":[20],"generalization,":[21],"it":[22,187],"necessary":[24],"control":[26,92],"the":[27,30,44,47,93,98,109,134,142,166,182],"\"complexity\"":[28],"learned":[31],"function.":[32],"In":[33,112,172],"Bayesian":[34,54],"approaches,":[35],"this":[36],"done":[38],"by":[39,129,141,153],"adopting":[40],"prior":[42],"for":[43],"parameters":[45,69,102],"function":[48],"being":[49],"learned.":[50],"We":[51],"propose":[52],"approach":[55,115,168],"learning,":[58],"which":[59,67,91,137],"leads":[60],"sparse":[62,80],"solutions;":[63],"that":[64,165],"is,":[65],"in":[66],"irrelevant":[68],"are":[70],"automatically":[71],"exactly":[73],"zero.":[75],"Other":[76],"ways":[77],"obtain":[79],"classifiers":[81],"(such":[82],"as":[83],"Laplacian":[84,135],"priors,":[85],"support":[86],"vector":[87],"machines)":[88],"involve":[89,118],"(hyper)parameters":[90,120],"degree":[94],"sparseness":[96],"resulting":[99],"classifiers;":[100],"these":[101],"have":[103],"be":[105,122],"somehow":[106],"adjusted/estimated":[107],"from":[108],"data.":[111],"contrast,":[113],"our":[114,174],"does":[116],"not":[117],"any":[119],"adjusted":[123],"or":[124,191],"estimated.":[125],"This":[126],"achieved":[128],"hierarchical-Bayes":[131],"interpretation":[132],"prior,":[136],"then":[139],"modified":[140],"adoption":[143],"Jeffreys'":[146],"noninformative":[147],"hyperprior.":[148],"Implementation":[149],"carried":[151],"out":[152],"an":[154],"expectation-maximization":[155],"(EM)":[156],"algorithm.":[157],"Experiments":[158],"with":[159,181],"several":[160],"benchmark":[161],"data":[162],"sets":[163],"show":[164],"proposed":[167],"yields":[169],"state-of-the-art":[170],"performance.":[171],"particular,":[173],"method":[175],"outperforms":[176],"SVMs":[177],"and":[178],"performs":[179],"competitively":[180],"best":[183],"alternative":[184],"techniques,":[185],"although":[186],"involves":[188],"no":[189],"tuning":[190],"adjustment":[192],"sparseness-controlling":[194],"hyperparameters.":[195]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":20},{"year":2020,"cited_by_count":13},{"year":2019,"cited_by_count":27},{"year":2018,"cited_by_count":22},{"year":2017,"cited_by_count":28},{"year":2016,"cited_by_count":28},{"year":2015,"cited_by_count":25},{"year":2014,"cited_by_count":27},{"year":2013,"cited_by_count":35},{"year":2012,"cited_by_count":38}],"updated_date":"2026-11-15T12:55:33.944570","created_date":"2016-06-24T00:00:00"}