{"id":"https://openalex.org/W2888857997","doi":"https://doi.org/10.1109/ssp.2018.8450750","title":"Asymptotic Behavior Of Margin-Based Classification Methods","display_name":"Asymptotic Behavior Of Margin-Based Classification Methods","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2888857997","doi":"https://doi.org/10.1109/ssp.2018.8450750","mag":"2888857997"},"language":"en","primary_location":{"id":"doi:10.1109/ssp.2018.8450750","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2018.8450750","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","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/A5018496840","display_name":"Hanwen Huang","orcid":"https://orcid.org/0000-0003-2021-755X"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hanwen Huang","raw_affiliation_strings":["Department of Epidemiology and Biostatistics, University of Georgia, GA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Epidemiology and Biostatistics, University of Georgia, GA, USA","institution_ids":["https://openalex.org/I165733156"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5018496840"],"corresponding_institution_ids":["https://openalex.org/I165733156"],"apc_list":null,"apc_paid":null,"fwci":0.3258,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.67332827,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"106","issue":null,"first_page":"463","last_page":"467"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9979000091552734,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9979000091552734,"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":0.9894000291824341,"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/T11716","display_name":"Random Matrices and Applications","score":0.9887999892234802,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.8105769157409668},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.803433895111084},{"id":"https://openalex.org/keywords/limit","display_name":"Limit (mathematics)","score":0.6142186522483826},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5783697962760925},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5411182641983032},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5374228954315186},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5052178502082825},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.48902830481529236},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.46977680921554565},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.46671101450920105},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4625965654850006},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4046333432197571},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3509078025817871},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.32100191712379456},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30056148767471313},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06353321671485901}],"concepts":[{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.8105769157409668},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.803433895111084},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.6142186522483826},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5783697962760925},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5411182641983032},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5374228954315186},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5052178502082825},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.48902830481529236},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.46977680921554565},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.46671101450920105},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4625965654850006},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4046333432197571},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3509078025817871},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.32100191712379456},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30056148767471313},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06353321671485901},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssp.2018.8450750","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2018.8450750","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6100000143051147,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1934446750","https://openalex.org/W1998894275","https://openalex.org/W2007527993","https://openalex.org/W2086623929","https://openalex.org/W2106525823","https://openalex.org/W2614751510","https://openalex.org/W2748584504","https://openalex.org/W2765732224","https://openalex.org/W2998768810","https://openalex.org/W3010187774","https://openalex.org/W6640393884"],"related_works":["https://openalex.org/W3125011624","https://openalex.org/W1508631387","https://openalex.org/W2370917603","https://openalex.org/W2952760143","https://openalex.org/W2017776670","https://openalex.org/W2347897961","https://openalex.org/W2979236518","https://openalex.org/W2090763504","https://openalex.org/W2358318464","https://openalex.org/W2379140333"],"abstract_inverted_index":{"We":[0],"investigate":[1],"the":[2,6,11,43,81,101],"asymptotic":[3],"behavior":[4],"of":[5,13,45,48],"margin-based":[7],"classification":[8,49,60],"methods":[9],"in":[10,90,108],"limit":[12],"large":[14,20],"dimension":[15],"p":[16],"\u2192":[17,24],"\u221e":[18,25],"and":[19,66,84,112],"sample":[21],"size":[22],"n":[23],"at":[26],"fixed":[27],"rate":[28],"=":[29],"n/p.":[30],"Under":[31],"spiked":[32],"population":[33],"model,":[34],"we":[35,52],"first":[36],"derive":[37],"a":[38,46,97],"general":[39],"framework":[40,55],"for":[41],"describing":[42],"performance":[44,87],"class":[47],"methods.":[50],"Then":[51],"apply":[53],"this":[54],"to":[56,80,100],"two":[57],"commonly":[58],"used":[59],"methods:":[61],"Support":[62],"Vector":[63],"Machine":[64],"(SVM)":[65],"Distance":[67],"Weighted":[68],"Discrimination":[69],"(DWD).":[70],"Our":[71],"analytical":[72],"results":[73,103],"show":[74],"that":[75,104],"DWD":[76],"is":[77],"less":[78],"sensitive":[79],"tuning":[82],"parameter":[83],"achieves":[85],"better":[86],"than":[88],"SVM":[89],"situations":[91],"where":[92],"n<;p.":[93],"This":[94],"finding":[95],"provides":[96],"theoretical":[98],"confirmation":[99],"empirical":[102],"have":[105],"been":[106],"observed":[107],"many":[109],"previous":[110],"simulation":[111],"real":[113],"data":[114],"studies.":[115]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
