{"id":"https://openalex.org/W2136701549","doi":null,"title":"Forward-Backward Activation Algorithm for Hierarchical Hidden Markov Models","display_name":"Forward-Backward Activation Algorithm for Hierarchical Hidden Markov Models","publication_year":2012,"publication_date":"2012-12-03","ids":{"openalex":"https://openalex.org/W2136701549","mag":"2136701549"},"language":"en","primary_location":{"id":"mag:2136701549","doi":null,"is_oa":false,"landing_page_url":"https://papers.nips.cc/paper/2012/file/8df707a948fac1b4a0f97aa554886ec8-Paper.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Neural Information Processing Systems","raw_type":null},"volume":null,"type":"article","is_traditional":true,"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/A5100887101","display_name":"Kei Wakabayashi","orcid":"https://orcid.org/0000-0001-6898-4833","observed_orcids":["https://orcid.org/0000-0001-6898-4833"]},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kei Wakabayashi","raw_affiliation_strings":["Faculty of Library, Information and Media Science, University of Tsukuba, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Library, Information and Media Science, University of Tsukuba, Japan","institution_ids":["https://openalex.org/I146399215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102278157","display_name":"Takao Miura","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I204291657","display_name":"Hosei University","ror":"https://ror.org/00bx6dj65","country_code":"JP","type":"education","lineage":["https://openalex.org/I204291657"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takao Miura","raw_affiliation_strings":["Department of Engineering, Hosei University, Japan#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Engineering, Hosei University, Japan#TAB#","institution_ids":["https://openalex.org/I204291657"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.872,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.75374841,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":96},"biblio":{"volume":"25","issue":null,"first_page":"1493","last_page":"1501"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.5273000001907349,"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":0.5273000001907349,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.3752000033855438,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.015699999406933784,"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/hierarchical-hidden-markov-models","display_name":"hierarchical hidden Markov models","score":0.9990000128746033},{"id":"https://openalex.org/keywords/forward-backward-algorithm","display_name":"forward-backward algorithm","score":0.9800000190734863},{"id":"https://openalex.org/keywords/model-inference","display_name":"model inference","score":0.6650000214576721},{"id":"https://openalex.org/keywords/parameter-estimation","display_name":"parameter estimation","score":0.949999988079071},{"id":"https://openalex.org/keywords/computational-complexity","display_name":"computational complexity","score":0.6679999828338623},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.984000027179718}],"concepts":[{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.6770796179771423},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6700689792633057},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6614993214607239},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.6513633131980896},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.6016353964805603},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5828959345817566},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.5539443492889404},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5168931484222412},{"id":"https://openalex.org/C196455857","wikidata":"https://www.wikidata.org/wiki/Q5473264","display_name":"Forward algorithm","level":5,"score":0.5095603466033936},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.5004251003265381},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.4903535842895508},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.46852725744247437},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4598257541656494},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.45277056097984314},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.438316285610199},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.37990981340408325},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27613741159439087},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.2718425393104553},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.24735555052757263},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.22767838835716248},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.08047476410865784},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C34447519","wikidata":"https://www.wikidata.org/wiki/Q179522","display_name":"Market economy","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2136701549","doi":null,"is_oa":false,"landing_page_url":"https://papers.nips.cc/paper/2012/file/8df707a948fac1b4a0f97aa554886ec8-Paper.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Neural Information Processing Systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1483867890","https://openalex.org/W1539673959","https://openalex.org/W1636244751","https://openalex.org/W1663973292","https://openalex.org/W1732623802","https://openalex.org/W2001082470","https://openalex.org/W2054038204","https://openalex.org/W2125838338","https://openalex.org/W2146143523","https://openalex.org/W2150196753","https://openalex.org/W2152177292","https://openalex.org/W2161874168","https://openalex.org/W2616071181"],"related_works":["https://openalex.org/W2146143523","https://openalex.org/W2150196753","https://openalex.org/W2560874752","https://openalex.org/W7100372822","https://openalex.org/W1554672230","https://openalex.org/W4242197974","https://openalex.org/W2153331007","https://openalex.org/W1607023980","https://openalex.org/W1636244751","https://openalex.org/W7154300033"],"abstract_inverted_index":{"Hierarchical":[0],"Hidden":[1],"Markov":[2],"Models":[3],"(HHMMs)":[4],"are":[5],"sophisticated":[6],"stochastic":[7],"models":[8],"that":[9,127],"enable":[10],"us":[11,115],"to":[12,92,116,125,134],"capture":[13],"a":[14,67,99,104],"hierarchical":[15,109],"context":[16],"characterization":[17],"of":[18,30,44,51,71,83,88,98,107,112],"sequence":[19,60],"data.":[20],"However,":[21],"existing":[22,141],"HHMM":[23,136],"parameter":[24],"estimation":[25],"methods":[26,142],"require":[27],"large":[28],"computations":[29],"time":[31,76],"complexity":[32,77],"O(TN2D)":[33],"at":[34],"least":[35],"for":[36,73],"model":[37,118],"inference,":[38],"where":[39],"D":[40],"is":[41,48,58,78,86],"the":[42,45,49,59,75,89,108,145],"depth":[43],"hierarchy,":[46],"N":[47],"number":[50],"states":[52],"in":[53],"each":[54],"level,":[55],"and":[56,148],"T":[57],"length.":[61],"In":[62],"this":[63],"paper,":[64],"we":[65],"propose":[66],"new":[68],"inference":[69,119],"method":[70,130,147],"HHMMs":[72],"which":[74,102],"O(TND+1).":[79],"A":[80],"key":[81],"idea":[82],"our":[84,128],"algorithm":[85,91],"application":[87],"forward-backward":[90],"state":[93,100],"activation":[94],"probabilities.":[95],"The":[96],"notion":[97],"activation,":[101],"offers":[103],"simple":[105],"formalization":[106],"transition":[110],"behavior":[111],"HHMMs,":[113],"enables":[114],"conduct":[117],"efficiently.":[120],"We":[121],"present":[122],"some":[123,140],"experiments":[124],"demonstrate":[126],"proposed":[129],"works":[131],"more":[132],"efficiently":[133],"estimate":[135],"parameters":[137],"than":[138],"do":[139],"such":[143],"as":[144],"flattening":[146],"Gibbs":[149],"sampling":[150],"method.":[151]},"counts_by_year":[{"year":2019,"cited_by_count":2},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2016-06-24T00:00:00"}