Complexities of feature-based learning systems, with application to reservoir computing

IF 6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks Pub Date : 2024-11-09 DOI:10.1016/j.neunet.2024.106883

Hiroki Yasumoto, Toshiyuki Tanaka

引用次数: 0

Abstract

This paper studies complexity measures of reservoir systems. For this purpose, a more general model that we call a feature-based learning system, which is the composition of a feature map and of a final estimator, is studied. We study complexity measures such as growth function, VC-dimension, pseudo-dimension and Rademacher complexity. On the basis of the results, we discuss how the unadjustability of reservoirs and the linearity of readouts can affect complexity measures of the reservoir systems. Furthermore, some of the results generalize or improve the existing results.

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基于特征的学习系统的复杂性，并将其应用于水库计算。

本文研究水库系统的复杂性度量。为此，我们研究了一个更一般的模型，我们称之为基于特征的学习系统，它是特征图和最终估计器的组合。我们研究了增长函数、VC 维度、伪维度和拉德马赫复杂性等复杂性度量。在这些结果的基础上，我们讨论了水库的不可调整性和读数的线性如何影响水库系统的复杂度。此外，一些结果还概括或改进了现有结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Neural Networks 工程技术-计算机：人工智能

CiteScore

13.90

自引率

7.70%

发文量

425

审稿时长

67 days

期刊介绍： Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.