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    ICASSP
    2009
    IEEE
    260views Signal Processing» more  ICASSP 2009»
    Blind sparse source separation for unknown number of sources using Gaussian mixture model fitting with Dirichlet prior
    13 years 3 months ago
    Blind sparse source separation for unknown number of sources using Gaussian mixture model fitting with Dirichlet prior
    Download  www.kecl.ntt.co.jp
    In this paper, we propose a novel sparse source separation method that can be applied even if the number of sources is unknown. Recently, many sparse source separation approaches ...
    Shoko Araki, Tomohiro Nakatani, Hiroshi Sawada, Sh...
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    KDD
    2007
    ACM
    237views Data Mining» more  KDD 2007»
    Knowledge discovery of multiple-topic document using parametric mixture model with dirichlet prior
    14 years 5 months ago
    Knowledge discovery of multiple-topic document using parametric mixture model with dirichlet prior
    Download  www.r.dl.itc.u-tokyo.ac.jp
    Documents, such as those seen on Wikipedia and Folksonomy, have tended to be assigned with multiple topics as a meta-data. Therefore, it is more and more important to analyze a re...
    Issei Sato, Hiroshi Nakagawa
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    ISMB
    1993
    113views Computational Biology» more  ISMB 1993»
    Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families
    13 years 6 months ago
    Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families
    Download  reference.kfupm.edu.sa
    A Bayesian method for estimating the amino acid distributions in the states of a hidden Markov model (HMM) for a protein familyor the columns of a multiple alignment of that famil...
    Michael Brown, Richard Hughey, Anders Krogh, I. Sa...
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    NIPS
    2003
    132views Information Technology» more  NIPS 2003»
    Ambiguous Model Learning Made Unambiguous with 1/f Priors
    13 years 6 months ago
    Ambiguous Model Learning Made Unambiguous with 1/f Priors
    Download  books.nips.cc
    What happens to the optimal interpretation of noisy data when there exists more than one equally plausible interpretation of the data? In a Bayesian model-learning framework the a...
    Gurinder S. Atwal, William Bialek
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    JMLR
    2010
    137views more  JMLR 2010»
    Covariance in Unsupervised Learning of Probabilistic Grammars
    13 years 7 days ago
    Covariance in Unsupervised Learning of Probabilistic Grammars
    Download  jmlr.csail.mit.edu
    Probabilistic grammars offer great flexibility in modeling discrete sequential data like natural language text. Their symbolic component is amenable to inspection by humans, while...
    Shay B. Cohen, Noah A. Smith
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