{"id":"https://openalex.org/W4387870718","doi":"https://doi.org/10.1109/mlsp55844.2023.10285889","title":"A Probabilistic Semi-Supervised Approach with Triplet Markov Chains","display_name":"A Probabilistic Semi-Supervised Approach with Triplet Markov Chains","publication_year":2023,"publication_date":"2023-09-17","ids":{"openalex":"https://openalex.org/W4387870718","doi":"https://doi.org/10.1109/mlsp55844.2023.10285889"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp55844.2023.10285889","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/mlsp55844.2023.10285889","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)","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/A5027794871","display_name":"Katherine Morales","orcid":"https://orcid.org/0000-0002-1871-4808"},"institutions":[{"id":"https://openalex.org/I4210145102","display_name":"Institut Polytechnique de Paris","ror":"https://ror.org/042tfbd02","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210145102"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Katherine Morales","raw_affiliation_strings":["Samovar, Telecom Sudparis, Institut Polytechnique de Paris"],"affiliations":[{"raw_affiliation_string":"Samovar, Telecom Sudparis, Institut Polytechnique de Paris","institution_ids":["https://openalex.org/I4210145102"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001420728","display_name":"Yohan Petetin","orcid":"https://orcid.org/0000-0001-9200-783X"},"institutions":[{"id":"https://openalex.org/I4210145102","display_name":"Institut Polytechnique de Paris","ror":"https://ror.org/042tfbd02","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210145102"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Yohan Petetin","raw_affiliation_strings":["Samovar, Telecom Sudparis, Institut Polytechnique de Paris"],"affiliations":[{"raw_affiliation_string":"Samovar, Telecom Sudparis, Institut Polytechnique de Paris","institution_ids":["https://openalex.org/I4210145102"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5027794871"],"corresponding_institution_ids":["https://openalex.org/I4210145102"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13579073,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"335","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9995999932289124,"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":0.9995999932289124,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9966999888420105,"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/markov-chain","display_name":"Markov chain","score":0.6446942687034607},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6397615671157837},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6270080208778381},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5814449191093445},{"id":"https://openalex.org/keywords/generality","display_name":"Generality","score":0.5676140785217285},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5606999397277832},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5264497399330139},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5043786764144897},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.48012495040893555},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4782199561595917},{"id":"https://openalex.org/keywords/variable-order-markov-model","display_name":"Variable-order Markov model","score":0.4550909996032715},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.45143869519233704},{"id":"https://openalex.org/keywords/parameterized-complexity","display_name":"Parameterized complexity","score":0.44862914085388184},{"id":"https://openalex.org/keywords/variable-order-bayesian-network","display_name":"Variable-order Bayesian network","score":0.4403942823410034},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.4268667697906494},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.42286744713783264},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.41801419854164124},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.40867680311203003},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3002127707004547},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.26212793588638306},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1260865032672882}],"concepts":[{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.6446942687034607},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6397615671157837},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6270080208778381},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5814449191093445},{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.5676140785217285},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5606999397277832},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5264497399330139},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5043786764144897},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.48012495040893555},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4782199561595917},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.4550909996032715},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.45143869519233704},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.44862914085388184},{"id":"https://openalex.org/C71983512","wikidata":"https://www.wikidata.org/wiki/Q7915687","display_name":"Variable-order Bayesian network","level":4,"score":0.4403942823410034},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.4268667697906494},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.42286744713783264},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.41801419854164124},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.40867680311203003},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3002127707004547},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26212793588638306},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1260865032672882},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp55844.2023.10285889","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/mlsp55844.2023.10285889","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1959608418","https://openalex.org/W1973318723","https://openalex.org/W1986628418","https://openalex.org/W2000721204","https://openalex.org/W2125838338","https://openalex.org/W2219995598","https://openalex.org/W2330149175","https://openalex.org/W2547875792","https://openalex.org/W2548228487","https://openalex.org/W2898695436","https://openalex.org/W2970975917","https://openalex.org/W3103009287","https://openalex.org/W4237840503","https://openalex.org/W4246784033","https://openalex.org/W4292183549","https://openalex.org/W6631190155","https://openalex.org/W6640963894","https://openalex.org/W6729448088","https://openalex.org/W6730091202","https://openalex.org/W6731935091","https://openalex.org/W6755553561"],"related_works":["https://openalex.org/W2616125534","https://openalex.org/W2963987720","https://openalex.org/W3022187094","https://openalex.org/W2146310005","https://openalex.org/W2963790827","https://openalex.org/W2786010873","https://openalex.org/W4293659211","https://openalex.org/W1985514205","https://openalex.org/W2048052024","https://openalex.org/W4385889111"],"abstract_inverted_index":{"Triplet":[0],"Markov":[1,83],"chains":[2],"are":[3],"general":[4,71],"generative":[5,105],"models":[6,85,106],"for":[7,101,107],"sequential":[8,108],"data":[9],"which":[10,28],"take":[11],"into":[12],"account":[13],"three":[14],"kinds":[15],"of":[16,34,62,92,104],"random":[17],"variables:":[18],"(noisy)":[19],"observations,":[20],"their":[21,38],"associated":[22,39,54],"discrete":[23],"labels":[24,53],"and":[25,37],"latent":[26],"variables":[27],"aim":[29],"at":[30,48],"strengthening":[31],"the":[32,35,52,56,60],"distribution":[33],"observations":[36,57],"labels.":[40],"However,":[41],"in":[42,86],"practice,":[43],"we":[44,68],"do":[45],"not":[46],"have":[47],"our":[49,93],"disposal":[50],"all":[51],"to":[55,58,79,97],"estimate":[59],"parameters":[61],"such":[63],"models.":[64],"In":[65],"this":[66],"paper,":[67],"propose":[69],"a":[70,75,87,102],"framework":[72],"based":[73],"on":[74],"variational":[76],"Bayesian":[77,109],"inference":[78],"train":[80],"parameterized":[81],"triplet":[82],"chain":[84],"semi-supervised":[88],"context.":[89],"The":[90],"generality":[91],"approach":[94],"enables":[95],"us":[96],"derive":[98],"semisupervised":[99],"algorithms":[100],"variety":[103],"classification.":[110]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}