FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3197886166> ?p ?o ?g. }

• W3197886166 endingPage "1551" @default.
• W3197886166 startingPage "1537" @default.
• W3197886166 abstract "The hidden Markov model (HMM) is a broadly applied generative model for representing time-series data, and clustering HMMs attract increased interest from machine learning researchers. However, the number of clusters ( K ) and the number of hidden states ( S ) for cluster centers are still difficult to determine. In this article, we propose a novel HMM-based clustering algorithm, the variational Bayesian hierarchical EM algorithm, which clusters HMMs through their densities and priors and simultaneously learns posteriors for the novel HMM cluster centers that compactly represent the structure of each cluster. The numbers K and S are automatically determined in two ways. First, we place a prior on the pair (K,S) and approximate their posterior probabilities, from which the values with the maximum posterior are selected. Second, some clusters and states are pruned out implicitly when no data samples are assigned to them, thereby leading to automatic selection of the model complexity. Experiments on synthetic and real data demonstrate that our algorithm performs better than using model selection techniques with maximum likelihood estimation." @default.
• W3197886166 created "2021-09-13" @default.
• W3197886166 creator A5024808218 @default.
• W3197886166 creator A5065680386 @default.
• W3197886166 creator A5066496436 @default.
• W3197886166 creator A5079353328 @default.
• W3197886166 creator A5091324533 @default.
• W3197886166 date "2023-03-01" @default.
• W3197886166 modified "2023-10-18" @default.
• W3197886166 title "Clustering Hidden Markov Models With Variational Bayesian Hierarchical EM" @default.
• W3197886166 cites W1981796042 @default.
• W3197886166 cites W2033872649 @default.
• W3197886166 cites W2033906329 @default.
• W3197886166 cites W2057590097 @default.
• W3197886166 cites W2057765075 @default.
• W3197886166 cites W2083875149 @default.
• W3197886166 cites W2086699924 @default.
• W3197886166 cites W2102122585 @default.
• W3197886166 cites W2109328551 @default.
• W3197886166 cites W2127498532 @default.
• W3197886166 cites W2133824856 @default.
• W3197886166 cites W2135291511 @default.
• W3197886166 cites W2138467869 @default.
• W3197886166 cites W2138665083 @default.
• W3197886166 cites W2142635246 @default.
• W3197886166 cites W2143203814 @default.
• W3197886166 cites W2160255741 @default.
• W3197886166 cites W2165835468 @default.
• W3197886166 cites W2168175751 @default.
• W3197886166 cites W2175288397 @default.
• W3197886166 cites W2394984844 @default.
• W3197886166 cites W2607336455 @default.
• W3197886166 cites W2610486050 @default.
• W3197886166 cites W2751963410 @default.
• W3197886166 cites W2783776928 @default.
• W3197886166 cites W2792572138 @default.
• W3197886166 cites W2803696563 @default.
• W3197886166 cites W2898790230 @default.
• W3197886166 cites W2901111836 @default.
• W3197886166 cites W2962851448 @default.
• W3197886166 cites W2964057981 @default.
• W3197886166 cites W2964822006 @default.
• W3197886166 cites W2986930344 @default.
• W3197886166 cites W2998870589 @default.
• W3197886166 cites W3012978776 @default.
• W3197886166 cites W3027009492 @default.
• W3197886166 cites W3036953619 @default.
• W3197886166 cites W3044271047 @default.
• W3197886166 cites W3049492921 @default.
• W3197886166 cites W3087258756 @default.
• W3197886166 cites W3101380508 @default.
• W3197886166 cites W3130539453 @default.
• W3197886166 cites W3159240214 @default.
• W3197886166 cites W3172508555 @default.
• W3197886166 cites W4213009331 @default.
• W3197886166 cites W4235169531 @default.
• W3197886166 cites W4252331534 @default.
• W3197886166 doi "https://doi.org/10.1109/tnnls.2021.3105570" @default.
• W3197886166 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34464269" @default.
• W3197886166 hasPublicationYear "2023" @default.
• W3197886166 type Work @default.
• W3197886166 sameAs 3197886166 @default.
• W3197886166 citedByCount "6" @default.
• W3197886166 countsByYear W31978861662021 @default.
• W3197886166 countsByYear W31978861662023 @default.
• W3197886166 crossrefType "journal-article" @default.
• W3197886166 hasAuthorship W3197886166A5024808218 @default.
• W3197886166 hasAuthorship W3197886166A5065680386 @default.
• W3197886166 hasAuthorship W3197886166A5066496436 @default.
• W3197886166 hasAuthorship W3197886166A5079353328 @default.
• W3197886166 hasAuthorship W3197886166A5091324533 @default.
• W3197886166 hasConcept C107673813 @default.
• W3197886166 hasConcept C11413529 @default.
• W3197886166 hasConcept C119857082 @default.
• W3197886166 hasConcept C153180895 @default.
• W3197886166 hasConcept C154945302 @default.
• W3197886166 hasConcept C164866538 @default.
• W3197886166 hasConcept C167966045 @default.
• W3197886166 hasConcept C177769412 @default.
• W3197886166 hasConcept C199360897 @default.
• W3197886166 hasConcept C23224414 @default.
• W3197886166 hasConcept C39890363 @default.
• W3197886166 hasConcept C41008148 @default.
• W3197886166 hasConcept C57830394 @default.
• W3197886166 hasConcept C61224824 @default.
• W3197886166 hasConcept C73555534 @default.
• W3197886166 hasConcept C81917197 @default.
• W3197886166 hasConcept C92835128 @default.
• W3197886166 hasConcept C93959086 @default.
• W3197886166 hasConceptScore W3197886166C107673813 @default.
• W3197886166 hasConceptScore W3197886166C11413529 @default.
• W3197886166 hasConceptScore W3197886166C119857082 @default.
• W3197886166 hasConceptScore W3197886166C153180895 @default.
• W3197886166 hasConceptScore W3197886166C154945302 @default.
• W3197886166 hasConceptScore W3197886166C164866538 @default.
• W3197886166 hasConceptScore W3197886166C167966045 @default.
• W3197886166 hasConceptScore W3197886166C177769412 @default.
• W3197886166 hasConceptScore W3197886166C199360897 @default.

• next