Modeling Solutions of Axisymmetric Contact Problems Using Dimensionality Reduction Method with or without Adhesion

Janahmadov, A.Kh.; Дышин, О. А.

doi:10.3103/s1068366618010075

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2023

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“…Zhang [18] proposed an autoencoder-SVM model based on the deep learning method of autoencoder neural network. However, they did not study the dimensionality reduction method [19] after dimensionality increase, so the specific convergence effect and classification accuracy are not yet clear. Therefore, based on the aforementioned research status, this paper designs two single-hidden-layer networks to further improve the accuracy and learning convergence speed of structured multi-class datasets.…”

Section: Introductionmentioning

confidence: 99%

A mapping and principal component dimensionality reduction analysis neural network for structured multi-classification

YE,

Tian,

Yang

et al. 2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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Structured multi-classification is a typical supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. This problem is often plagued by the slow training speed and the low classification accuracy. The goal of this paper is to propose a novel framework called normalizing quadratic Mapping and principal component Dimensionality Reduction analysis (MDR) Neural Network. Thorough experiments demonstrate it sets the new state of the art, accelerating the convergence of neural network and improving the accuracy of classification recognition with a few number of training rounds.

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Section: Introductionmentioning

confidence: 99%