{"id":"https://openalex.org/W2165875847","doi":"https://doi.org/10.1109/fskd.2011.6019661","title":"Adaptation of Gaussian ARD kernel for multiclass classification","display_name":"Adaptation of Gaussian ARD kernel for multiclass classification","publication_year":2011,"publication_date":"2011-07-01","ids":{"openalex":"https://openalex.org/W2165875847","doi":"https://doi.org/10.1109/fskd.2011.6019661","mag":"2165875847"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2011.6019661","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2011.6019661","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)","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/A5113882147","display_name":"Tinghua Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210119042","display_name":"Gannan Normal University","ror":"https://ror.org/02jf7e446","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210119042"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tinghua Wang","raw_affiliation_strings":["School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, PR China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, PR China","institution_ids":["https://openalex.org/I4210119042"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5113882147"],"corresponding_institution_ids":["https://openalex.org/I4210119042"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.18999768,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"5","issue":null,"first_page":"983","last_page":"986"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9986000061035156,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9986000061035156,"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/T12676","display_name":"Machine Learning and ELM","score":0.9986000061035156,"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/T10320","display_name":"Neural Networks and Applications","score":0.9973000288009644,"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","display_name":"Kernel (algebra)","score":0.7525337934494019},{"id":"https://openalex.org/keywords/gaussian-function","display_name":"Gaussian function","score":0.6413302421569824},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6267396807670593},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5792944431304932},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5538361668586731},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5220324993133545},{"id":"https://openalex.org/keywords/multiclass-classification","display_name":"Multiclass classification","score":0.5067582726478577},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.48821142315864563},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4613044857978821},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.4505314826965332},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.42843541502952576},{"id":"https://openalex.org/keywords/kernel-embedding-of-distributions","display_name":"Kernel embedding of distributions","score":0.42424753308296204},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.418820858001709},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.40803971886634827},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3482711911201477},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.23960831761360168},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.07759660482406616}],"concepts":[{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.7525337934494019},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.6413302421569824},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6267396807670593},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5792944431304932},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5538361668586731},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5220324993133545},{"id":"https://openalex.org/C123860398","wikidata":"https://www.wikidata.org/wiki/Q6934605","display_name":"Multiclass classification","level":3,"score":0.5067582726478577},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.48821142315864563},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4613044857978821},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.4505314826965332},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.42843541502952576},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.42424753308296204},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.418820858001709},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40803971886634827},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3482711911201477},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.23960831761360168},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.07759660482406616},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fskd.2011.6019661","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2011.6019661","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2169610","https://openalex.org/W1510073064","https://openalex.org/W1602492977","https://openalex.org/W1931885984","https://openalex.org/W1965350027","https://openalex.org/W2108710077","https://openalex.org/W2114217318","https://openalex.org/W2115521056","https://openalex.org/W2133864802","https://openalex.org/W2152353853","https://openalex.org/W2153104898","https://openalex.org/W2153635508","https://openalex.org/W2156909104","https://openalex.org/W2158001550","https://openalex.org/W2172000360","https://openalex.org/W3014813097","https://openalex.org/W3120421331","https://openalex.org/W3120740533","https://openalex.org/W4312326823","https://openalex.org/W6600089577","https://openalex.org/W6680007464"],"related_works":["https://openalex.org/W2366185040","https://openalex.org/W2098028537","https://openalex.org/W4221107122","https://openalex.org/W2140869420","https://openalex.org/W4375846106","https://openalex.org/W2958374823","https://openalex.org/W2558026684","https://openalex.org/W2382704364","https://openalex.org/W2375370983","https://openalex.org/W4311472972"],"abstract_inverted_index":{"The":[0,101],"problem":[1],"of":[2,54,72,97],"optimizing":[3],"Gaussian":[4,18,26,73],"Automatic":[5],"Relevance":[6],"(ARD)":[7],"kernel":[8,19,28,45,56,75],"in":[9,57],"a":[10,21,40,55,63,85],"multiclass":[11,58],"setting":[12],"is":[13,83,104],"considered.":[14],"Unlike":[15],"the":[16,25,34,52,69,78,94,98],"conventional":[17],"with":[20,106],"single":[22],"width":[23,70],"parameter,":[24],"ARD":[27,74],"adopts":[29],"multiple":[30],"widths":[31],"corresponding":[32,99],"to":[33,50,67],"input":[35],"features.":[36],"We":[37],"first":[38],"present":[39],"model":[41],"selection":[42],"criterion":[43],"named":[44],"distance-based":[46],"class":[47],"separability":[48],"(KDCS)":[49],"evaluate":[51],"goodness":[53],"classification":[59],"scenario,":[60],"then":[61],"propose":[62],"gradient-based":[64],"optimization":[65],"algorithm":[66],"tune":[68],"parameters":[71],"via":[76],"maximizing":[77],"KDCS":[79],"criterion.":[80],"This":[81],"method":[82,88,103],"essentially":[84],"feature":[86],"weighting":[87],"since":[89],"each":[90],"learned":[91],"parameter":[92],"indicates":[93],"relative":[95],"importance":[96],"feature.":[100],"proposed":[102],"demonstrated":[105],"some":[107],"UCI":[108],"machine":[109],"learning":[110],"benchmark":[111],"examples.":[112]},"counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}