Entropy Based Evolutionary Search for Feature Selection

Subbotin, Sergey; Oleynik, A.

doi:10.1109/cadsm.2007.4297612

Cited by 7 publications

(1 citation statement)

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“…We will rewrite matrix A by grouping its elements not by rows but by columns, and in this way, we will form a new matrix: (10) Let's denote by the matrix transposition operator, which corresponds to the transition from ( 9) to (10), then…”

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Analysis of the main provisions of the theory of parallel-hierarchical transformations

Tymchenko,

Stetsenko,

Kokryatskay

et al. 2023

Opt-el. Inf-Power Techn.

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The article presents an analysis of the main principles of parallel-hierarchical transformations theory. The continuous movement of society towards the automation of everyday life requires the creation of fundamentally new software and hardware solutions. Considering the current physical limitations of integrated circuits, it is evident that improving software processing is the way to go. The main problem lies in the increasing complexity of architecture and supporting such code. The ideas of parallel-hierarchical networks allow for a significant increase in processing speed through process parallelization while maintaining the relative simplicity of the software solution's architecture. The proposed structure of the parallel-hierarchical network allows for modelling the operation principle of a distributed neural network and forms a deterministic network using spatial-temporal division. The general rules of direct and inverse parallel-hierarchical transformation and their application to image recognition tasks are discussed. A block diagram of the algorithm for the basic model of nonlinear direct network transformation is shown. A mathematical model of direct parallel-hierarchical transformation is presented using an example. Compared to known numerical transformation methods involving simple operations like addition, the model enables complex functional signal processing in real-time scale, as well as unambiguity and reversibility with good convergence of the computational process.

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confidence: 99%