Neuromorphic tuning of feature spaces to overcome the challenge of low-sample high-dimensional data-Reference-Cited by-同舟云学术

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Neuromorphic tuning of feature spaces to overcome the challenge of low-sample high-dimensional data

Published:2023-06-18 Issue: Volume: Page:
ISSN:
Container-title:2023 International Joint Conference on Neural Networks (IJCNN)
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Author:

Zhou Qinghua¹,Sutton Oliver J.¹,Zhang Yu-Dong²,Gorban Alexander N.²,Makarov Valeri A.³,Tyukin Ivan Y.¹

Affiliation:

1. King's College London,Department of Mathematics,London,UK

2. School of Computing and Mathematical Sciences University of Leicester,Leicester,UK

3. Complutense University of Madrid,Department of Applied Mathematics and Mathematical Analysis,Madrid,Spain

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10190990/10190992/10191304.pdf?arnumber=10191304

Reference16 articles.

1. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

2. Blessing of dimensionality: mathematical foundations of the statistical physics of data

3. Breast cancer histopathological image classification using Convolutional Neural Networks

4. Lung and colon cancer histopathological image dataset (lc25000);borkowski;ArXiv Preprint,2019

5. Recent advances in the deep CNN neocognitron

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1. A fully interpretable stacking fuzzy classifier with stochastic configuration-based learning for high-dimensional data;Information Sciences;2025-01

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