{"id":"https://openalex.org/W2736043274","doi":"https://doi.org/10.1109/ijcnn.2017.7965850","title":"Weibull partition models with applications to hidden semi-Markov models","display_name":"Weibull partition models with applications to hidden semi-Markov models","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2736043274","doi":"https://doi.org/10.1109/ijcnn.2017.7965850","mag":"2736043274"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2017.7965850","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7965850","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","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/A5109377230","display_name":"Youwei Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Youwei Lu","raw_affiliation_strings":["Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080920610","display_name":"Shogo Okada","orcid":"https://orcid.org/0000-0002-9260-0403"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shogo Okada","raw_affiliation_strings":["School of Computing, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan"],"affiliations":[{"raw_affiliation_string":"School of Computing, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109912424","display_name":"Katsumi Nitta","orcid":"https://orcid.org/0000-0002-9018-8603"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Katsumi Nitta","raw_affiliation_strings":["School of Computing, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan"],"affiliations":[{"raw_affiliation_string":"School of Computing, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan","institution_ids":["https://openalex.org/I114531698"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5109377230"],"corresponding_institution_ids":["https://openalex.org/I114531698"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0845724,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":null,"first_page":"162","last_page":"169"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9998999834060669,"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/T11901","display_name":"Bayesian Methods and Mixture Models","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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9779000282287598,"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":0.9725000262260437,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/weibull-distribution","display_name":"Weibull distribution","score":0.8665450811386108},{"id":"https://openalex.org/keywords/hidden-semi-markov-model","display_name":"Hidden semi-Markov model","score":0.7087388634681702},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.6102708578109741},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.5631529688835144},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48578742146492004},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4834743142127991},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.47830918431282043},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.46218597888946533},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.44621214270591736},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4375552237033844},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4267522096633911},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4254583716392517},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36516350507736206},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.32505691051483154},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2653205394744873},{"id":"https://openalex.org/keywords/variable-order-markov-model","display_name":"Variable-order Markov model","score":0.2251138985157013},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.0748257040977478},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.07008510828018188}],"concepts":[{"id":"https://openalex.org/C173291955","wikidata":"https://www.wikidata.org/wiki/Q732332","display_name":"Weibull distribution","level":2,"score":0.8665450811386108},{"id":"https://openalex.org/C64939953","wikidata":"https://www.wikidata.org/wiki/Q3859882","display_name":"Hidden semi-Markov model","level":5,"score":0.7087388634681702},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.6102708578109741},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.5631529688835144},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48578742146492004},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4834743142127991},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.47830918431282043},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46218597888946533},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.44621214270591736},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4375552237033844},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4267522096633911},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4254583716392517},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36516350507736206},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.32505691051483154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2653205394744873},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.2251138985157013},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.0748257040977478},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.07008510828018188}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2017.7965850","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7965850","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","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":32,"referenced_works":["https://openalex.org/W1498269992","https://openalex.org/W1539673959","https://openalex.org/W1880262756","https://openalex.org/W2029873015","https://openalex.org/W2033828039","https://openalex.org/W2047102900","https://openalex.org/W2076618452","https://openalex.org/W2080972498","https://openalex.org/W2091797506","https://openalex.org/W2118036030","https://openalex.org/W2122740842","https://openalex.org/W2124585778","https://openalex.org/W2125838338","https://openalex.org/W2128709346","https://openalex.org/W2132991226","https://openalex.org/W2136019741","https://openalex.org/W2141461755","https://openalex.org/W2151967501","https://openalex.org/W2158266063","https://openalex.org/W2483430316","https://openalex.org/W2615915281","https://openalex.org/W2949115596","https://openalex.org/W2972891548","https://openalex.org/W4231510805","https://openalex.org/W6629892778","https://openalex.org/W6634986005","https://openalex.org/W6639619044","https://openalex.org/W6678103038","https://openalex.org/W6678435566","https://openalex.org/W6679356491","https://openalex.org/W6684363390","https://openalex.org/W6767874007"],"related_works":["https://openalex.org/W1510894296","https://openalex.org/W2379938888","https://openalex.org/W4246505579","https://openalex.org/W2134386692","https://openalex.org/W2103649304","https://openalex.org/W2116722627","https://openalex.org/W2799426416","https://openalex.org/W2537260108","https://openalex.org/W1977445474","https://openalex.org/W2792905593"],"abstract_inverted_index":{"We":[0],"develop":[1],"the":[2,25,30,58,61,73,79,86,98,110,126,137,142,148],"Weibull":[3,33,43,45,62,80],"partition":[4,34,63,81],"model":[5,35,76,82],"(WPM),":[6],"which":[7],"defines":[8],"a":[9,37,42,48,117],"novel":[10],"nonparametric":[11],"stochastic":[12],"process":[13,39],"over":[14],"distributions":[15,46],"of":[16,18,27,60,68,100,103,113],"partitions":[17],"sequential":[19],"data,":[20],"aiming":[21],"at":[22],"directly":[23],"modeling":[24,160],"boundaries":[26,162],"segments":[28],"comprising":[29],"sequence.":[31],"The":[32,91],"employs":[36],"Dirichlet":[38],"mixture":[40],"with":[41,85],"kernel.":[44],"having":[47,116],"closed-form":[49],"cumulative":[50],"density":[51],"function":[52],"plays":[53],"an":[54,66],"important":[55],"role":[56],"in":[57,155],"construction":[59],"model.":[64],"As":[65],"application":[67,143],"our":[69,121],"model,":[70],"we":[71,123],"propose":[72],"hidden":[74,106,114,145],"semi-Markov":[75],"based":[77],"on":[78],"(WPM-HSMM),":[83],"along":[84],"corresponding":[87],"recursive":[88],"sampling":[89],"algorithm.":[90],"WPM-HSMM":[92,127],"can":[93,150],"be":[94,152],"used":[95],"to":[96,136,144],"solve":[97],"problem":[99],"low":[101],"accuracy":[102],"inference":[104],"for":[105],"states,":[107],"caused":[108],"by":[109],"holding":[111],"times":[112],"states":[115],"complicated":[118],"distribution.":[119],"In":[120],"experiments,":[122],"show":[124],"that":[125],"rapidly":[128],"mixes":[129],"and":[130],"achieves":[131],"very":[132],"competitive":[133],"results":[134],"compared":[135],"state-of-the-art":[138],"algorithms.":[139],"Apart":[140],"from":[141],"Markov":[146],"models,":[147],"WPM":[149],"also":[151],"found":[153],"useful":[154],"any":[156],"problem,":[157],"where":[158],"explicitly":[159],"segment":[161],"is":[163],"needed.":[164]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}