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Multilevel classifiers based on a tree-structured set of Gaussian densities
Pattern Recognition and Image Analysis. 2014. Vol. 24. No. 3. P. 443–451.
N.A. Novikov
This paper considers an approach to solving the problem of binary classification of objects. This approach is based on representing one of the classes by a sequence of Gaussian mixtures with further introduction of threshold decision rules. A method of constructing hierarchical sequences of Gaussian mixtures using the partial EM algorithm is proposed. We compare classifiers that use single Gaussian mixtures, cascades based on sequences of independent mixtures, cascades based on hierarchical sequences of mixtures, and classifiers that use trees of Gaussian densities for decision making. The theoretical estimates of computational costs for these classifiers are provided. The classifiers are tested on simulated data. The results are presented as the relations between the computational cost of classification and the obtained values of error criteria.
Priority areas:
IT and mathematics
Language:
English
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Vorontsov K. V., Potapenko A., Machine Learning 2015 Vol. 101 No. 1 P. 303–323
Probabilistic topic modeling of text collections has been recently developed mainly within the framework of graphical models and Bayesian inference. In this paper we introduce an alternative semi-probabilistic approach, which we call additive regularization of topic models (ARTM). Instead of building a purely probabilistic generative model of text we regularize an ill-posed problem of stochastic matrix factorization ...
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Vorontsov K. V., Potapenko A., Машинное обучение и анализ данных 2013 Т. 1 № 6 С. 657–686
Probabilistic topic models discover a low-dimensional interpretable representation of text corpora by estimating a multinomial distribution over topics for each document and a multinomial distribution over terms for each topic. A uni ed family of expectation-maximization (EM) like algorithms with smoothing, sampling, sparsing, and robustness heuristics that can be used in any combinations is considered. The ...
Added: February 19, 2015
Vorontsov K. V., Potapenko A., Компьютерные исследования и моделирование 2012 Т. 4 № 4 С. 693–706
We propose a generalized probabilistic topic model of text corpora which can incorporate heuristics of Bayesian regularization, sampling, frequent parameters update, and robustness in any combinations. Well- known models PLSA, LDA, CVB0, SWB, and many others can be considered as special cases of the proposed broad family of models. We propose the robust PLSA model ...
Added: February 19, 2015
Konstantin Vorontsov, Anna Potapenko, , in: Communications in Computer and Information ScienceVol. 436: Analysis of Images, Social Networks and Texts. Third International Conference, AIST 2014 Yekaterinburg, Russia, April 10–12, 2014 Revised Selected Papers.: Cham: Springer, 2014. P. 29–46.
Probabilistic topic modeling of text collections is a powerful tool for statistical text analysis. In this tutorial we introduce a novel non-Bayesian approach, called Additive Regularization of Topic Models. ARTM is free of redundant probabilistic assumptions and provides a simple inference for many combined and multi-objective topic models. ...
Added: December 5, 2014
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In this paper we consider the behavior of Kalman Filter state estimates in the case of distribution with heavy tails .The simulated linear state space models with Gaussian measurement noises were used. Gaussian noises in state equation are replaced by components with alpha-stable distribution with different parameters alpha and beta. We consider the case when ...
Added: November 14, 2014
К.В. Воронцов, Потапенко А. А., Машинное обучение и анализ данных 2013 Т. 1 № 6 С. 657–686
Probabilistic topic models discover a low-dimensional interpretable representation of text corpora
by estimating a multinomial distribution over topics for each document and a multinomial
distribution over terms for each topic. A uni ed family of expectation-maximization (EM) like
algorithms with smoothing, sampling, sparsing, and robustness heuristics that can be used in
any combinations is considered. The known models PLSA (probabilistic ...
Added: May 6, 2014
Lange, M. M., Novikov A. Nikita, Pattern Recognition and Image Analysis 2012 Vol. 22 No. 1 P. 136–143
A novel approach is proposed to constructing a Bayes classifier in a multidimensional space of fea
tures by using treestructured Gaussian mixtures as estimates of classconditional probability density func
tions. A training procedure is developed for the classifier that is reduced to finding numbers of mixture com
ponents and their thresholds in order to realize rejections for the ...
Added: December 3, 2013
Lange, M. M., Novikov A. Nikita, , in: Proceedings of 10th International Conference on Pattern Recognition and Image Analysis (PRIA-2010, St. Petersburg, Russia)Vol. 1.: Politechnika, 2010. P. 153–156.
Multidimensional Bayes Classifier Based on Tree-Structured Gaussian Mixtures ...
Added: December 3, 2013