{"id":"https://openalex.org/W7096536673","doi":null,"title":"Semisupervised Classifier Evaluation and","display_name":"Semisupervised Classifier Evaluation and","publication_year":2012,"publication_date":"2012-01-01","ids":{"openalex":"https://openalex.org/W7096536673"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.761.8713","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.761.8713","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1210.2162.pdf","raw_type":"text"},"volume":null,"type":"preprint","indexed_in":[],"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/A5010674841","display_name":"Peter Welinder","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peter Welinder","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087368991","display_name":"Max Welling","orcid":"https://orcid.org/0000-0003-1484-2121","observed_orcids":["https://orcid.org/0000-0003-1484-2121"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Max Welling","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5026825380","display_name":"Pietro Perona","orcid":"https://orcid.org/0000-0002-7583-5809","observed_orcids":["https://orcid.org/0000-0002-7583-5809"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pietro Perona","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9115999937057495,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9115999937057495,"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":0.0706000030040741,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.009499999694526196,"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/confidence-calibration","display_name":"confidence calibration","score":0.5559999942779541},{"id":"https://openalex.org/keywords/confidence-limits","display_name":"confidence limits","score":0.5379999876022339}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.8245999813079834},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.713699996471405},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.644599974155426},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6420000195503235},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4934000074863434},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49079999327659607},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.4375},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.43459999561309814},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4212999939918518},{"id":"https://openalex.org/C2909755999","wikidata":"https://www.wikidata.org/wiki/Q4751126","display_name":"Low Confidence","level":2,"score":0.3871000111103058},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.37549999356269836},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.32589998841285706},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.28999999165534973}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.761.8713","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.761.8713","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1210.2162.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W1668901377","https://openalex.org/W2139990538","https://openalex.org/W4406745202","https://openalex.org/W1513690018","https://openalex.org/W2385190297","https://openalex.org/W7098047080","https://openalex.org/W2136504847","https://openalex.org/W2762929692","https://openalex.org/W2368412068","https://openalex.org/W1737790567"],"abstract_inverted_index":{"How":[0],"many":[1],"labeled":[2],"examples":[3],"are":[4,25],"needed":[5],"to":[6,45,82,95],"estimate":[7,46],"a":[8,12,32,53,72,97],"classifier\u2019s":[9,77],"per-formance":[10],"on":[11,36,71,88],"new":[13,89],"dataset?":[14],"We":[15,27],"study":[16],"the":[17,37,40,76,84,101],"case":[18],"where":[19],"data":[20],"is":[21,43,69],"plentiful,":[22],"but":[23],"labels":[24],"expensive.":[26],"show":[28],"that":[29],"by":[30,99],"making":[31],"few":[33],"reasonable":[34],"assump-tions":[35],"structure":[38],"of":[39,56,86],"data,":[41],"it":[42],"possible":[44],"performance":[47,85],"curves,":[48],"with":[49],"confidence":[50,78,103],"bounds,":[51],"using":[52],"small":[54],"number":[55],"ground":[57],"truth":[58],"labels.":[59],"Our":[60],"approach,":[61],"which":[62],"we":[63],"call":[64],"Semisupervised":[65],"Performance":[66],"Evalu-ation":[67],"(SPE),":[68],"based":[70],"generative":[73],"model":[74],"for":[75],"scores.":[79],"In":[80],"addition":[81],"estimating":[83],"classifiers":[87],"datasets,":[90],"SPE":[91],"can":[92],"be":[93],"used":[94],"recalibrate":[96],"classifier":[98],"re-estimating":[100],"class-conditional":[102],"distributions.":[104],"1":[105]},"counts_by_year":[],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2025-10-27T00:00:00"}