Перегляд за автором "Deineko, A. O."

Зараз показано 1 - 3 з 3
  • Зображення мініатюри
    Публікація
    Evolving flexible neuro-fuzzy system for medical diagnostic tasks
    (IJCSMC, 2015) Turuta, O.; Perova, I.; Deineko, A. O.
    In the paper architecture and training method for evolving flexible diagnostic neuro-fuzzy-system are investigated. The proposed system is simple in numeric realization and characterized by a high learning rate and flexibility, that make possible to use it in conditions of small training sets and on big data sets, coming to processing in online-mode.
  • Зображення мініатюри
    Публікація
    Evolving Neural Network for Kernel Principal Component Analysis
    (IJCSMC, 2015) Turuta, O.; Deineko, A. O.; Perova, I.; Kutsenko, Y.; Shalamov, M.
    In the paper kernel evolving neural network and its learning algorithm are investigated. The proposed system solves the problem of finding the eigenvectors and the corresponding principal components in on-line mode in an environment where hidden in the experimental data interdependencies are nonlinear and can change throw time.
  • Немає доступних мініатюр
    Публікація
    Kernel principal component analysis in data stream mining tasks
    (2016) Bodyanskiy, Ye. V.; Deineko, A. O.; Eze, F. M.; Shalamov, M. O.
    Currently, self-learning systems of computational intelligence [1, 2] and, above all , artificial neural networks (ANN ), that tune their parameters without a teacher on the basis of the self-learning paradigm [3], are widely used in solving various problems of Data Mining, Exploratory Data Analysis etc. Among these tasks, most frequently encountered in the Text Mining, Web Mining, Medical Data Mining, it be can mentioned the problem of compression of large data sets, for whose solution principal component analysis (PCA) is widely used, which consists in the orthogonal projection of input data vectors from the original n-dimensional space in the m- dimensional space of reduced dimensionality