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» Dirichlet Process Mixtures of Generalized Linear Models
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ICCV
2007
IEEE
16 years 1 months ago
Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes
We develop nonparametric Bayesian models for multiscale representations of images depicting natural scene categories. Individual features or wavelet coefficients are marginally de...
Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jord...
CSDA
2007
137views more  CSDA 2007»
14 years 11 months ago
Fitting finite mixtures of generalized linear regressions in R
R package flexmix provides flexible modelling of finite mixtures of regression models using the EM algorithm. Several new features of the software such as fixed and nested var...
Bettina Grün, Friedrich Leisch
CVPR
2008
IEEE
16 years 1 months ago
Incremental learning of nonparametric Bayesian mixture models
Clustering is a fundamental task in many vision applications. To date, most clustering algorithms work in a batch setting and training examples must be gathered in a large group b...
Ryan Gomes, Max Welling, Pietro Perona
SAC
2011
ACM
14 years 6 months ago
Slice sampling mixture models
We propose a more efficient version of the slice sampler for Dirichlet process mixture models described by Walker (2007). This sampler allows the fitting of infinite mixture mod...
Maria Kalli, Jim E. Griffin, Stephen G. Walker