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Volumn 4212 LNAI, Issue , 2006, Pages 246-257

Deconvolutive clustering of Markov states

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; DATA STRUCTURES; DATABASE SYSTEMS; LEARNING ALGORITHMS; PROBABILITY; SIGNAL PROCESSING;

EID: 33750325168     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11871842_26     Document Type: Conference Paper
Times cited : (3)

References (21)
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    • An information maximisation approach to blind separation and blind deconvolution
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    • Berchtold, A.1    Raftery, A.E.2
  • 5
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    • Topic identification in dynamical text by complexity pursuit
    • E Bingham, A Kaban and M Girolami. Topic Identification in Dynamical Text by Complexity Pursuit, Neural Processing Letters 17: 1-15, 2003, pp. 69-83.
    • (2003) Neural Processing Letters , vol.17 , Issue.1-15 , pp. 69-83
    • Bingham, E.1    Kaban, A.2    Girolami, M.3
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    • Uniformization and hypergraph partitioning for the distributed computation of response time densities in very large Markov models
    • N.J Dingle, P.G Harrison and W.J Knottenbelt. Uniformization and Hypergraph Partitioning for the Distributed Computation of Response Time Densities in Very Large Markov Models. Journal of Parallel and Distributed Computing, 64, 2004, pp. 908-920.
    • (2004) Journal of Parallel and Distributed Computing , vol.64 , pp. 908-920
    • Dingle, N.J.1    Harrison, P.G.2    Knottenbelt, W.J.3
  • 13
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    • Detecting community structure in networks
    • M.E.J Newman. Detecting community structure in networks. Euro. Phys. Journal B, 38: 321-330, 2004.
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    • Newman, M.E.J.1
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    • 0032596518 scopus 로고    scopus 로고
    • Mixed memory Markov models: Decomposing complex stochastic processes as mixtures of simpler ones
    • L.K Saul and M.I Jordan. Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones. Machine Learning, 37(1):75-87, 1999.
    • (1999) Machine Learning , vol.37 , Issue.1 , pp. 75-87
    • Saul, L.K.1    Jordan, M.I.2
  • 18
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    • Blind deconvolution using temporal predictability
    • J.V. Stone: Blind deconvolution using temporal predictability. Neurocomputing 49(1-4): 79-86 (2002)
    • (2002) Neurocomputing , vol.49 , Issue.1-4 , pp. 79-86
    • Stone, J.V.1
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    • A tutorial on hidden Markov models And selected applications in speech recognition
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    • Rabiner, L.R.1
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    • Statistical mechanics and phase transitions in clustering
    • K Rose. E. Gurewitz and G.C Fox. Statistical Mechanics and Phase Transitions in Clustering. Phys. Rev. Letters, vol. 65, no. 8, pp.945-948.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.