Finite Generalization of the Offline Spectral Learning

Kotsovsky, Vladyslav; Geche, Fedir; Batyuk, Anatoliy

doi:10.1109/dsmp.2018.8478584

Cited by 17 publications

(1 citation statement)

References 6 publications

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“…The zoom level is defined by the number of items which can be displayed on the view without scrolling. The software developed can be used during various data mining tasks [1,3,14,15,19].…”

Section: Principal Component Analysismentioning

confidence: 99%

An Integral Software Solution of the SGTM Neural-Like Structures Implementation for Solving Different Data Mining Tasks

Tkachenko

2021

Lecture Notes on Data Engineering and Communications Technologies

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The paper presents a developed software solution that implements a new learning model and application of artificial neural networks, i.e. the Successive Geometric Transformations Model, to solve various applied data mining tasks. This model is applied to construct Feed Forward Neural Networks, which anticipates the rejection of training goal interpretation as a multi-extremal optimization procedure. The modes of use of the system developed (prediction, time series, and cascade modes) are described in detail and illustrated, as well as the structure and functions of separate constituents of the graphical user interface; data selection and download procedures; neuro-like structure parameterization capabilities for each different mode of operation of the program. Simulation of the developed integral software solution was performed while fulfilling prediction, classification, and time series forecasting. Besides, the feasibility of performing nonlinear PCA analysis at high speed is shown. When performing all the above tasks, the program outcomes are presented in both numerical form and using the appropriate one for a particular mode of visualization in 2D or 3D formats.

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“…The zoom level is defined by the number of items which can be displayed on the view without scrolling. The software developed can be used during various data mining tasks [1,3,14,15,19].…”

Section: Principal Component Analysismentioning

confidence: 99%