{"id":"https://openalex.org/W1989896330","doi":"https://doi.org/10.1109/icassp.2010.5495362","title":"Sample-separation-margin based minimum classification error training of pattern classifiers with quadratic discriminant functions","display_name":"Sample-separation-margin based minimum classification error training of pattern classifiers with quadratic discriminant functions","publication_year":2010,"publication_date":"2010-01-01","ids":{"openalex":"https://openalex.org/W1989896330","doi":"https://doi.org/10.1109/icassp.2010.5495362","mag":"1989896330"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2010.5495362","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2010.5495362","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Conference on Acoustics, Speech and Signal Processing","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/A5100339974","display_name":"Yongqiang Wang","orcid":"https://orcid.org/0000-0002-0240-9374"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]},{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["CN","HK"],"is_corresponding":true,"raw_author_name":"Yongqiang Wang","raw_affiliation_strings":["Department of Computer Science, University of Hong Kong, Hong Kong, China","Microsoft Research Asia, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]},{"raw_affiliation_string":"Microsoft Research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039662070","display_name":"Qiang Huo","orcid":"https://orcid.org/0000-0003-2464-6482"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Huo","raw_affiliation_strings":["Microsoft Research Asia, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100339974"],"corresponding_institution_ids":["https://openalex.org/I4210113369","https://openalex.org/I889458895"],"apc_list":null,"apc_paid":null,"fwci":4.2026,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.9443024,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"5","issue":null,"first_page":"1866","last_page":"1869"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9991000294685364,"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":0.9991000294685364,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9968000054359436,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9955999851226807,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7165657877922058},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.6759242415428162},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6470808982849121},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.622988760471344},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.601963996887207},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5497413873672485},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5219318866729736},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5197134017944336},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.5012478828430176},{"id":"https://openalex.org/keywords/quadratic-classifier","display_name":"Quadratic classifier","score":0.48658812046051025},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.47610175609588623},{"id":"https://openalex.org/keywords/quadratic-equation","display_name":"Quadratic equation","score":0.46618741750717163},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.40405911207199097},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3288533091545105},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.3273937702178955},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.23935163021087646}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7165657877922058},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.6759242415428162},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6470808982849121},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.622988760471344},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.601963996887207},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5497413873672485},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5219318866729736},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5197134017944336},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.5012478828430176},{"id":"https://openalex.org/C52620605","wikidata":"https://www.wikidata.org/wiki/Q7268357","display_name":"Quadratic classifier","level":3,"score":0.48658812046051025},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.47610175609588623},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.46618741750717163},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.40405911207199097},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3288533091545105},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3273937702178955},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.23935163021087646},{"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icassp.2010.5495362","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2010.5495362","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Conference on Acoustics, Speech and Signal Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.221.9990","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.221.9990","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://mi.eng.cam.ac.uk/%7Eyw293/pdfs/ICASSP10.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6899999976158142,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2010581677","https://openalex.org/W2040193698","https://openalex.org/W2063541597","https://openalex.org/W2065846434","https://openalex.org/W2091850740","https://openalex.org/W2093790790","https://openalex.org/W2098084448","https://openalex.org/W2134969019","https://openalex.org/W2152876238","https://openalex.org/W2158289097","https://openalex.org/W2280164830","https://openalex.org/W2645468781","https://openalex.org/W6682848589"],"related_works":["https://openalex.org/W2113454941","https://openalex.org/W2951122819","https://openalex.org/W4288315282","https://openalex.org/W2350751952","https://openalex.org/W2362114017","https://openalex.org/W1999647744","https://openalex.org/W2088711785","https://openalex.org/W3147024994","https://openalex.org/W4285246984","https://openalex.org/W2075660794"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"present":[4],"a":[5,22,53,64,97],"new":[6],"approach":[7],"to":[8,37,110],"minimum":[9],"classification":[10,83],"error":[11,113],"(MCE)":[12],"training":[13,32,77,89],"of":[14,47],"pattern":[15],"classifiers":[16],"with":[17,103],"quadratic":[18],"discriminant":[19],"functions.":[20],"First,":[21],"so-called":[23],"sample":[24,33],"separation":[25],"margin":[26],"(SSM)":[27],"is":[28,100,116],"defined":[29],"for":[30],"each":[31],"and":[34,58],"then":[35],"used":[36],"define":[38],"the":[39,81,88,93],"misclassification":[40],"measure":[41],"in":[42,118],"MCE":[43,76,105],"formulation.":[44],"The":[45],"computation":[46],"SSM":[48],"can":[49],"be":[50],"cast":[51],"as":[52],"nonlinear":[54],"constrained":[55],"optimization":[56],"problem":[57],"solved":[59],"efficiently.":[60],"Experimental":[61],"results":[62],"on":[63],"large-scale":[65],"isolated":[66],"online":[67],"handwritten":[68],"Chinese":[69],"character":[70],"recognition":[71],"task":[72],"demonstrate":[73],"that":[74],"SSM-based":[75],"not":[78],"only":[79],"decreases":[80],"empirical":[82],"error,":[84],"but":[85],"also":[86],"pushes":[87],"samples":[90],"away":[91],"from":[92],"decision":[94],"boundaries,":[95],"therefore":[96],"good":[98],"generalization":[99],"achieved.":[101],"Compared":[102],"conventional":[104],"training,":[106],"an":[107],"additional":[108],"7%":[109],"18%":[111],"relative":[112],"rate":[114],"reduction":[115],"observed":[117],"our":[119],"experiments.":[120]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":8},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
