Results 261 to 270 of about 3,472,659 (303)

On Clustering by Mixture Models

2003
Finite mixture models are being increasingly used to model the distributions of a wide variety of random phenomena and to cluster data sets; see, for example, McLachlan and Peel (2000a). We consider the use of normal mixture models to cluster data sets of continuous multivariate data, concentrating on some of the associated computational issues.
McLachlan, G. J., Ng, A.S. K., Peel, D.
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Robust Bayesian mixture modelling

Neurocomputing, 2005
Bayesian approaches to density estimation and clustering using mixture distributions allow the automatic determination of the number of components in the mixture. Previous treatments have focussed on mixtures having Gaussian components, but these are well known to be sensitive to outliers, which can lead to excessive sensitivity to small numbers of ...
BISHOP CHRISTOPHER M, SVENSEN JOHAN FREDRIK MARKUS   +1 more
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On Fitting Mixture Models

1999
Consider the problem of fitting a finite Gaussian mixture, with an unknown number of components, to observed data. This paper proposes a new minimum description length (MDL) type criterion, termed MMDL(f or mixture MDL), to select the number of components of the model.
Mário A. T. Figueiredo, José M. N. Leitão, Anil K. Jain 0001   +2 more
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Dirichlet Process Mixture of Mixtures Model for Unsupervised Subword Modeling

IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018
We develop a parallelizable Markov chain Monte Carlo sampler for a Dirichlet process mixture of mixtures model. Our sampler jointly infers a codebook and clusters. The codebook is a global collection of components. Clusters are mixtures, defined over the codebook.
Michael Heck, Sakriani Sakti, Satoshi Nakamura 0001   +2 more
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Mixture of mixture n-gram language models

2013 IEEE Workshop on Automatic Speech Recognition and Understanding, 2013
This paper presents a language model adaptation technique to build a single static language model from a set of language models each trained on a separate text corpus while aiming to maximize the likelihood of an adaptation data set given as a development set of sentences.
Hasim Sak, Cyril Allauzen, Kaisuke Nakajima, Françoise Beaufays   +3 more
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Design and Modeling Strategies for Mixture-of-Mixtures Experiments

Technometrics, 2011
In mixture-of-mixtures experiments major components are defined as the components which themselves are mixtures of some other components, called minor components. Sometimes components are divided into different categories where each category is called a major component and the components within a major component become minor components.
Lulu Kang, V. Roshan Joseph, William A. Brenneman   +2 more
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Mixture Models for Classification

2007
Finite mixture distributions provide efficient approaches of model-based clustering and classification. The advantages of mixture models for unsupervised classification are reviewed. Then, the article is focusing on the model selection problem. The usefulness of taking into account the modeling purpose when selecting a model is advocated in the ...
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Latent Dirichlet mixture model

Neurocomputing, 2018
Text representation based on latent topic model is seen as a non-Gaussian problem where the observed words and latent topics are multinomial variables and the topic proportionals are Dirichlet variables. Traditional topic model is established by introducing a single Dirichlet prior to characterize the topic proportionals.
Jen-Tzung Chien, Chao-Hsi Lee, Zheng-Hua Tan   +2 more
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Mixture Models of Categorization

Journal of Mathematical Psychology, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Multilevel Mixture Factor Models

Multivariate Behavioral Research, 2012
Factor analysis is a statistical method for describing the associations among sets of observed variables in terms of a small number of underlying continuous latent variables. Various authors have proposed multilevel extensions of the factor model for the analysis of data sets with a hierarchical structure.
Varriale R., Vermunt J. K.
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