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Of all publications in the section: 10
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Article
Кашницкий Ю. С., Игнатов Д. И. Интеллектуальные системы. Теория и приложения. 2015. Т. 19. № 4. С. 37-55.

The paper makes a brief introduction into multiple classifier systems and describes a particular algorithm which improves classification accuracy by making a recommendation of an algorithm to an object. This recommendation is done under a hypothesis that a classifier is likely to predict the label of the object correctly if it has correctly classified its neighbors. The process of assigning a classifier to each object involves here the apparatus of Formal Concept Analysis. We explain the principle of the algorithm on a toy example and describe experiments with real-world datasets.

Added: Dec 7, 2015
Article
Сысоева Л. Н. Интеллектуальные системы. Теория и приложения. 2016. Т. 20. № 4. С. 98-103.

The problem of realization of Boolean functions by initial Boolean automata with constant states and n inputs is considered. Initial Boolean automaton with constant states and n inputs is an initial automaton with output such that in all states output functions are n-ary constant Boolean functions 0 or 1. All sets of the maximum cardinality of n-ary Boolean functions realized by an initial Boolean automaton with two or three constant states provided to the possibility of an arbitrary order of input values is obtained.

Added: Feb 28, 2017
Article
Кириллов А. Н., Гавриков М. И., Лобачева Е. М. и др. Интеллектуальные системы. Теория и приложения. 2015. Т. 19. № 2. С. 75-95.

In this paper we consider the Shape Boltzmann Machine(SBM) and its multi-label version MSBM. We present an algorithm for training MSBM using only binary masks of objects and the seeds which approximately correspond to the locations of objects parts.  

Added: Sep 30, 2015
Article
Самоненко И. Ю. Интеллектуальные системы. Теория и приложения. 2007. № 11. С. 787-792.
Added: Sep 28, 2018
Article
Самоненко И. Ю., Волченков М. П. Интеллектуальные системы. Теория и приложения. 2005. Т. 9. С. 153-157.
Added: Sep 28, 2018
Article
Самоненко И. Ю. Интеллектуальные системы. Теория и приложения. 2007. № 11. С. 329-340.
Added: Sep 28, 2018
Article
Самоненко И. Ю. Интеллектуальные системы. Теория и приложения. 2018. Т. 22. № 2. С. 113-121.

A hyperautomatа is a finite automatа whose states are the sets of states of some finite automata.  A hyperautomatа is called a group hyperautomatа if the semigroup of the automatа on which it is based is a finite group. In this paper, we study the question of the maximum number of regular languages that can be recognized by group hyperautomata.

Added: Sep 28, 2018
Article
Новиков А. В., Родоманов А. О., Осокин А. А. и др. Интеллектуальные системы. Теория и приложения. 2014. Т. 18. № 4. С. 293-318.
Added: Oct 17, 2016
Article
Фигурнов М. В., Струминский К. А., Ветров Д. П. Интеллектуальные системы. Теория и приложения. 2017. Т. 21. № 2. С. 90-109.

Variational autoencoder (VAE) is a probabilistic unsupervised method that uses deep learning. We propose a robust approach to the training of VAE using a modified likelihood function. We propose and analyze two variational lower bound objectives. The effectiveness of the method is experimentally shown by artificially introducing noise objects.

Added: Oct 18, 2017
Article
Поляков Н. Л. Интеллектуальные системы. Теория и приложения. 2016. Т. 20. № 4. С. 70-75.

One of the main tasks of the theory of collective choice is formulated in the language of functional Galois correspondences. A convenient characterization of symmetric classes of decision rules without the Arrow property is proposed.

Added: Oct 6, 2018