{"id":"https://openalex.org/W2910396745","doi":"https://doi.org/10.1145/3297001.3297012","title":"Learning from Low Training Data using Classifiers with Derivative Constraints","display_name":"Learning from Low Training Data using Classifiers with Derivative Constraints","publication_year":2019,"publication_date":"2019-01-03","ids":{"openalex":"https://openalex.org/W2910396745","doi":"https://doi.org/10.1145/3297001.3297012","mag":"2910396745"},"language":"en","primary_location":{"id":"doi:10.1145/3297001.3297012","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3297001.3297012","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM India Joint International Conference on Data Science and Management of Data","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/A5083371463","display_name":"Sumit Soman","orcid":"https://orcid.org/0000-0003-1926-3065"},"institutions":[{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Sumit Soman","raw_affiliation_strings":["Department of Electrical Engineering, Indian Institute of Technology, Delhi, India"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Indian Institute of Technology, Delhi, India","institution_ids":["https://openalex.org/I68891433"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002842122","display_name":"Jayadeva","orcid":null},"institutions":[{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Jayadeva","raw_affiliation_strings":["Department of Electrical Engineering, Indian Institute of Technology, Delhi, India"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Indian Institute of Technology, Delhi, India","institution_ids":["https://openalex.org/I68891433"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5083371463"],"corresponding_institution_ids":["https://openalex.org/I68891433"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00634145,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"86","last_page":"93"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9995999932289124,"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":0.9995999932289124,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","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/T12535","display_name":"Machine Learning and Data Classification","score":0.9984999895095825,"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":0.814490556716919},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6693466305732727},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.656980037689209},{"id":"https://openalex.org/keywords/decision-boundary","display_name":"Decision boundary","score":0.643829345703125},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5914870500564575},{"id":"https://openalex.org/keywords/maxima-and-minima","display_name":"Maxima and minima","score":0.5897495746612549},{"id":"https://openalex.org/keywords/hyperplane","display_name":"Hyperplane","score":0.5860817432403564},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5288259387016296},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5057804584503174},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4999961853027344},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.49867701530456543},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4743864834308624},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4353402256965637},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4303862750530243},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33427339792251587},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2394450306892395}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.814490556716919},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6693466305732727},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.656980037689209},{"id":"https://openalex.org/C42023084","wikidata":"https://www.wikidata.org/wiki/Q5249231","display_name":"Decision boundary","level":3,"score":0.643829345703125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5914870500564575},{"id":"https://openalex.org/C186633575","wikidata":"https://www.wikidata.org/wiki/Q845060","display_name":"Maxima and minima","level":2,"score":0.5897495746612549},{"id":"https://openalex.org/C68693459","wikidata":"https://www.wikidata.org/wiki/Q657586","display_name":"Hyperplane","level":2,"score":0.5860817432403564},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5288259387016296},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5057804584503174},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4999961853027344},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.49867701530456543},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4743864834308624},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4353402256965637},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4303862750530243},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33427339792251587},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2394450306892395},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3297001.3297012","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3297001.3297012","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM India Joint International Conference on Data Science and Management of Data","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1563938718","https://openalex.org/W1580049798","https://openalex.org/W1628991974","https://openalex.org/W1965788850","https://openalex.org/W1988790447","https://openalex.org/W1998255116","https://openalex.org/W2030504055","https://openalex.org/W2043076104","https://openalex.org/W2119821739","https://openalex.org/W2133324003","https://openalex.org/W2149298154","https://openalex.org/W2153635508","https://openalex.org/W2294798173","https://openalex.org/W2621643135","https://openalex.org/W2746122193","https://openalex.org/W2789143659","https://openalex.org/W2973651390"],"related_works":["https://openalex.org/W4388952643","https://openalex.org/W2006908074","https://openalex.org/W2138963285","https://openalex.org/W2110654986","https://openalex.org/W2385299555","https://openalex.org/W1825786209","https://openalex.org/W2320105591","https://openalex.org/W2382071382","https://openalex.org/W1825889605","https://openalex.org/W2113853244"],"abstract_inverted_index":{"Availability":[0],"of":[1,23,60,67,95,110,121,140,163,166,191],"low":[2],"training":[3,18,37,69,112,183],"data":[4,33,51],"presents":[5],"a":[6,20,38,102,105,138],"challenge":[7],"in":[8,80,161],"several":[9,28],"learning":[10,48],"scenarios,":[11],"primarily":[12],"since":[13],"generalization":[14],"is":[15,34,115,184],"dependent":[16],"on":[17,57,72,144],"using":[19],"large":[21],"number":[22,165],"samples.":[24,70,113],"However,":[25],"there":[26],"are":[27,195],"practical":[29],"scenarios":[30],"when":[31,181],"limited":[32],"available":[35],"for":[36,47,197,199],"classifier.":[39],"In":[40],"this":[41,141],"paper,":[42],"we":[43,85],"present":[44,137],"an":[45,87],"approach":[46,131,142],"with":[49,186],"few":[50,188],"samples,":[52],"involving":[53],"additional":[54,88],"constraints":[55],"based":[56,143],"computing":[58],"derivatives":[59],"the":[61,65,68,73,93,96,108,111,119,122,132,145,151,172,182,192,203],"decision":[62],"boundary":[63],"at":[64,107],"location":[66],"Based":[71],"kernel":[74],"Support":[75],"Vector":[76],"Machine":[77,148,205],"(SVM)":[78],"formulation":[79],"Empirical":[81],"Feature":[82],"Space":[83],"(EFS),":[84],"add":[86],"constraint":[89,125],"which":[90,154],"stipulates":[91],"that":[92,171],"derivative":[94,120],"separating":[97],"hyperplane":[98],"corresponds":[99],"to":[100,126,157],"either":[101],"maxima":[103],"or":[104],"minima":[106],"locations":[109],"This":[114],"done":[116,185],"by":[117],"setting":[118],"conventional":[123,179],"SVM":[124],"zero.":[127],"We":[128,135,169],"call":[129],"our":[130],"SVM-Derivative":[133],"(SVM-D).":[134],"also":[136],"variant":[139],"Minimal":[146],"Complexity":[147],"(MCM),":[149],"called":[150],"MCM-Derivative":[152],"(MCM-D),":[153],"allows":[155],"us":[156],"realize":[158],"sparser":[159],"models":[160],"terms":[162],"fewer":[164],"support":[167],"vectors.":[168],"illustrate":[170],"proposed":[173],"approaches":[174],"perform":[175],"much":[176],"better":[177],"than":[178],"SVMs":[180],"very":[187],"samples":[189,194],"(20%":[190],"dataset":[193],"used":[196],"training)":[198],"various":[200],"datasets":[201],"from":[202],"UCI":[204],"Learning":[206],"repository.":[207]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
