{"id":"https://openalex.org/W2140621117","doi":"https://doi.org/10.1109/fuzzy.2008.4630645","title":"Maximum A Posteriori EM MCE Logistic LASSO for learning fuzzy measures","display_name":"Maximum A Posteriori EM MCE Logistic LASSO for learning fuzzy measures","publication_year":2008,"publication_date":"2008-06-01","ids":{"openalex":"https://openalex.org/W2140621117","doi":"https://doi.org/10.1109/fuzzy.2008.4630645","mag":"2140621117"},"language":"en","primary_location":{"id":"doi:10.1109/fuzzy.2008.4630645","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzzy.2008.4630645","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence)","raw_type":"proceedings-article"},"type":"article","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/A5052468718","display_name":"Andres M\u00e9ndez-V\u00e1zquez","orcid":"https://orcid.org/0000-0001-7121-8195"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Andres Mendez-Vazquez","raw_affiliation_strings":["Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA","Dept. of Comput. & Inf. Sci. & Eng., Univ. of Florida, Gainesville, FL"],"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA","institution_ids":["https://openalex.org/I33213144"]},{"raw_affiliation_string":"Dept. of Comput. & Inf. Sci. & Eng., Univ. of Florida, Gainesville, FL","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057277525","display_name":"Paul Gader","orcid":"https://orcid.org/0000-0001-6276-9403"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Paul Gader","raw_affiliation_strings":["Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA","Dept. of Comput. & Inf. Sci. & Eng., Univ. of Florida, Gainesville, FL"],"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA","institution_ids":["https://openalex.org/I33213144"]},{"raw_affiliation_string":"Dept. of Comput. & Inf. Sci. & Eng., Univ. of Florida, Gainesville, FL","institution_ids":["https://openalex.org/I33213144"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5052468718"],"corresponding_institution_ids":["https://openalex.org/I33213144"],"apc_list":null,"apc_paid":null,"fwci":0.5904,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.79825981,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"2007","last_page":"2013"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9740999937057495,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9740999937057495,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9603000283241272,"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/T11609","display_name":"Geophysical Methods and Applications","score":0.957099974155426,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.6738874316215515},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.5985113382339478},{"id":"https://openalex.org/keywords/lasso","display_name":"Lasso (programming language)","score":0.5373944044113159},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.527442991733551},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.509589433670044},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.5082054138183594},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5039662718772888},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.4837320148944855},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4678400754928589},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4478401839733124},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4456586539745331},{"id":"https://openalex.org/keywords/monotonic-function","display_name":"Monotonic function","score":0.4377685785293579},{"id":"https://openalex.org/keywords/fuzzy-set","display_name":"Fuzzy set","score":0.4197115898132324},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40700191259384155},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35723328590393066},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3404988944530487},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3209347724914551},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.1957075595855713},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14542359113693237}],"concepts":[{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.6738874316215515},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5985113382339478},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.5373944044113159},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.527442991733551},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.509589433670044},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.5082054138183594},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5039662718772888},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.4837320148944855},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4678400754928589},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4478401839733124},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4456586539745331},{"id":"https://openalex.org/C72169020","wikidata":"https://www.wikidata.org/wiki/Q194404","display_name":"Monotonic function","level":2,"score":0.4377685785293579},{"id":"https://openalex.org/C42011625","wikidata":"https://www.wikidata.org/wiki/Q1055058","display_name":"Fuzzy set","level":3,"score":0.4197115898132324},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40700191259384155},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35723328590393066},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3404988944530487},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3209347724914551},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.1957075595855713},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14542359113693237},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fuzzy.2008.4630645","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzzy.2008.4630645","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337390","display_name":"Division of Chemical, Bioengineering, Environmental, and Transport Systems","ror":"https://ror.org/0471zv972"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"},{"id":"https://openalex.org/F4320338295","display_name":"Army Research Laboratory","ror":"https://ror.org/011hc8f90"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W130710483","https://openalex.org/W171068589","https://openalex.org/W657067832","https://openalex.org/W1481142035","https://openalex.org/W1484881229","https://openalex.org/W1485433433","https://openalex.org/W1634072176","https://openalex.org/W1870293938","https://openalex.org/W1907738203","https://openalex.org/W1969855798","https://openalex.org/W2033810643","https://openalex.org/W2046223100","https://openalex.org/W2046933642","https://openalex.org/W2085908116","https://openalex.org/W2088195363","https://openalex.org/W2104299368","https://openalex.org/W2106638710","https://openalex.org/W2107090525","https://openalex.org/W2114396382","https://openalex.org/W2124073038","https://openalex.org/W2124793110","https://openalex.org/W2124957100","https://openalex.org/W2127377873","https://openalex.org/W2131595827","https://openalex.org/W2132257108","https://openalex.org/W2135046866","https://openalex.org/W2136796925","https://openalex.org/W2138845258","https://openalex.org/W2144364256","https://openalex.org/W2146571341","https://openalex.org/W2152977846","https://openalex.org/W2153232459","https://openalex.org/W2167053440","https://openalex.org/W2328227226","https://openalex.org/W2460519968","https://openalex.org/W2491908944","https://openalex.org/W3043771091","https://openalex.org/W6636683810","https://openalex.org/W6639918369","https://openalex.org/W6780661845"],"related_works":["https://openalex.org/W2114899076","https://openalex.org/W1783992599","https://openalex.org/W2124697778","https://openalex.org/W2135468550","https://openalex.org/W2133422797","https://openalex.org/W2045588782","https://openalex.org/W1976188970","https://openalex.org/W2181917637","https://openalex.org/W4284711868","https://openalex.org/W2162040150"],"abstract_inverted_index":{"A":[0],"novel":[1],"algorithm":[2,16,98],"is":[3,29,61,81,99],"introduced":[4],"for":[5,9],"learning":[6,70],"fuzzy":[7,59],"measures":[8],"Choquet":[10],"integral-based":[11],"information":[12],"fusion.":[13],"The":[14],"new":[15,97],"goes":[17],"beyond":[18],"previously":[19],"published":[20],"MCE-based":[21],"approaches.":[22],"It":[23],"has":[24],"the":[25,38,45,58,76,96,103,118,137,146],"advantage":[26],"that":[27],"it":[28],"applicable":[30],"to":[31,36,74,101],"general":[32],"measures,":[33],"as":[34,63,102],"opposed":[35],"only":[37],"Sugeno":[39],"class":[40],"of":[41,131,143,151],"measures.":[42],"In":[43,72],"addition,":[44],"monotonicity":[46],"constraints":[47],"are":[48,110,134],"handled":[49],"easily":[50],"with":[51,83],"minimal":[52],"time":[53],"or":[54],"storage":[55],"requirements.":[56],"Learning":[57],"measure":[60],"framed":[62],"a":[64,66,84,122],"maximum":[65],"posteriori":[67],"(MAP)":[68],"parameter":[69],"problem.":[71,125],"order":[73],"maintain":[75],"constraints,":[77],"this":[78],"MAP":[79],"problem":[80,140],"solved":[82],"Gibbs":[85],"sampler":[86],"using":[87],"an":[88],"expectation":[89],"maximization":[90],"(EM)":[91],"framework.":[92],"For":[93],"these":[94],"reasons,":[95],"referred":[100],"MAP-EM":[104],"MCE":[105],"logistic":[106],"LASSO":[107],"algorithm.":[108],"Results":[109],"given":[111],"on":[112,136],"synthetic":[113],"and":[114,141,148],"real":[115],"data":[116],"sets,":[117],"latter":[119],"obtained":[120],"from":[121],"landmine":[123,138],"detection":[124,139,144],"Average":[126],"reductions":[127],"in":[128,145],"false":[129],"alarms":[130],"about":[132],"25%":[133],"achieved":[135],"probabilities":[142],"interesting":[147],"meaningful":[149],"range":[150],"85%-95%.":[152]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}
