{"id":"https://openalex.org/W2500524438","doi":"https://doi.org/10.1109/iccomm.2016.7528302","title":"A super fast vector support classifier using novelty detection and no synaptic tuning","display_name":"A super fast vector support classifier using novelty detection and no synaptic tuning","publication_year":2016,"publication_date":"2016-06-01","ids":{"openalex":"https://openalex.org/W2500524438","doi":"https://doi.org/10.1109/iccomm.2016.7528302","mag":"2500524438"},"language":"en","primary_location":{"id":"doi:10.1109/iccomm.2016.7528302","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccomm.2016.7528302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Communications (COMM)","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/A5057919315","display_name":"Radu Dogaru","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Radu Dogaru","raw_affiliation_strings":["Applied Electronics and Information Engineering, Natural Computing Laboratory, Bucharest, Romania"],"affiliations":[{"raw_affiliation_string":"Applied Electronics and Information Engineering, Natural Computing Laboratory, Bucharest, Romania","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108522699","display_name":"Ioana Dogaru","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ioana Dogaru","raw_affiliation_strings":["Applied Electronics and Information Engineering, Natural Computing Laboratory, Bucharest, Romania"],"affiliations":[{"raw_affiliation_string":"Applied Electronics and Information Engineering, Natural Computing Laboratory, Bucharest, Romania","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5057919315"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.1417,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.95750896,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"373","last_page":"376"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9998999834060669,"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/T12676","display_name":"Machine Learning and ELM","score":0.9998999834060669,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9948999881744385,"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/support-vector-machine","display_name":"Support vector machine","score":0.7848328948020935},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7750635743141174},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7146192789077759},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6502295732498169},{"id":"https://openalex.org/keywords/novelty","display_name":"Novelty","score":0.6499254107475281},{"id":"https://openalex.org/keywords/novelty-detection","display_name":"Novelty detection","score":0.6180676817893982},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5162618160247803},{"id":"https://openalex.org/keywords/radial-basis-function","display_name":"Radial basis function","score":0.47889652848243713},{"id":"https://openalex.org/keywords/margin-classifier","display_name":"Margin classifier","score":0.46156448125839233},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.45889851450920105},{"id":"https://openalex.org/keywords/structured-support-vector-machine","display_name":"Structured support vector machine","score":0.45106014609336853},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45081526041030884},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3874109983444214}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7848328948020935},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7750635743141174},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7146192789077759},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6502295732498169},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.6499254107475281},{"id":"https://openalex.org/C2778924833","wikidata":"https://www.wikidata.org/wiki/Q7064603","display_name":"Novelty detection","level":3,"score":0.6180676817893982},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5162618160247803},{"id":"https://openalex.org/C98856871","wikidata":"https://www.wikidata.org/wiki/Q1588488","display_name":"Radial basis function","level":3,"score":0.47889652848243713},{"id":"https://openalex.org/C173102733","wikidata":"https://www.wikidata.org/wiki/Q6760396","display_name":"Margin classifier","level":3,"score":0.46156448125839233},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.45889851450920105},{"id":"https://openalex.org/C125168437","wikidata":"https://www.wikidata.org/wiki/Q7625184","display_name":"Structured support vector machine","level":3,"score":0.45106014609336853},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45081526041030884},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3874109983444214},{"id":"https://openalex.org/C27206212","wikidata":"https://www.wikidata.org/wiki/Q34178","display_name":"Theology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccomm.2016.7528302","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccomm.2016.7528302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Communications (COMM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.49000000953674316}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1925255416","https://openalex.org/W1984112290","https://openalex.org/W2031878488","https://openalex.org/W2100659887","https://openalex.org/W2104242071","https://openalex.org/W2112607270","https://openalex.org/W2121971770","https://openalex.org/W2133349950","https://openalex.org/W2153635508","https://openalex.org/W2163922914","https://openalex.org/W2582014579","https://openalex.org/W3120740533"],"related_works":["https://openalex.org/W2384390720","https://openalex.org/W2388218200","https://openalex.org/W3142456129","https://openalex.org/W1988416745","https://openalex.org/W2371356469","https://openalex.org/W4253128984","https://openalex.org/W597801233","https://openalex.org/W2130972437","https://openalex.org/W2359125414","https://openalex.org/W1925255416"],"abstract_inverted_index":{"A":[0],"novel":[1],"classifier":[2],"architecture":[3],"is":[4,59,72,117],"introduced":[5],"and":[6,70,112],"its":[7],"performances":[8],"are":[9],"evaluated":[10],"against":[11],"state":[12,80],"of":[13,36,55,81,123,126,143],"the":[14,39,53,56,82],"art":[15,83],"shallow":[16],"classifiers.":[17],"Its":[18],"main":[19],"advantage":[20],"consists":[21],"in":[22,45,64],"a":[23,29,34,46,109,140],"very":[24],"fast":[25,113],"learning":[26],"ensured":[27],"by":[28],"novelty":[30],"detection":[31],"algorithm,":[32],"selecting":[33],"list":[35],"prototypes":[37],"among":[38],"training":[40],"samples,":[41],"used":[42],"as":[43,86,129],"centers":[44],"radial":[47],"basis":[48,57],"functions":[49,58],"neurons":[50],"layer.":[51],"Only":[52],"radius":[54],"optimized":[60],"to":[61,79],"improve":[62],"generalization":[63],"conjunction":[65],"with":[66,139],"an":[67],"overlapping":[68],"parameter":[69],"there":[71],"no":[73],"need":[74],"for":[75,120],"synaptic":[76],"tuning.":[77],"Compared":[78],"models":[84],"such":[85,128],"SVM":[87],"(Support":[88],"Vector":[89,100],"Machine)":[90,95],"or":[91],"ELM":[92],"(Extreme":[93],"Learning":[94],"our":[96],"SFSVC":[97,116],"(Super":[98],"Fast":[99],"Support":[101],"Classifier)":[102],"it":[103],"offers":[104],"equal":[105],"performance":[106],"while":[107],"having":[108],"more":[110],"compact":[111],"algorithm.":[114],"Thus":[115],"well":[118],"suited":[119],"embedded":[121],"processing":[122],"big":[124],"collections":[125],"data":[127,130],"from":[131],"satellite":[132],"remote":[133],"sensing":[134,148],"units,":[135],"automotive":[136],"sensors":[137],"etc.":[138],"good":[141],"potential":[142],"being":[144],"directly":[145],"integrated":[146],"into":[147],"platforms.":[149]},"counts_by_year":[{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}