Convergence of online gradient methods for continuous perceptrons with linearly separable training patterns

Wu W.; Shao ZQ.

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Convergence of online gradient methods for continuous perceptrons with linearly separable training patterns

机译：具有线性可分离训练模式的连续感知器在线梯度方法的收敛性

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In this paper, we prove that the online gradient method for continuous perceptrons converges in finite steps when the training patterns are linearly separable. (C) 2003 Elsevier Ltd. All rights reserved. [References: 4]

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《Applied mathematics letters》 |2003年第7期|共4页
作者
Wu W.; Shao ZQ.;
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正文语种 eng
中图分类应用数学;
关键词
Feedforward neural networks; Online gradient method; Convergence; Linearly separable; Continuous perceptrons;

机译：前馈神经网络;在线梯度法;收敛;线性可分离;连续感知器;

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1. Convergence of online gradient methods for continuous perceptrons with linearly separable training patterns [J] . Wu W., Shao ZQ. Applied mathematics letters . 2003,第7期

机译：具有线性可分离训练模式的连续感知器在线梯度方法的收敛性
2. Boundedness and Convergence of Online Gradient Method with Penalty for Linear Output Feedforward Neural Networks [J] . Huisheng Zhang, Wei Wu Neural processing letters . 2009,第3期

机译：线性输出前馈神经网络惩罚性在线梯度法的有界性和收敛性
3. Complexity Issues in Natural Gradient Descent Method for Training Multilayer Perceptrons [J] . Yang H, Amari S Neural computation . 1998,第8期

机译：训练多层感知器的自然梯度下降方法中的复杂性问题
4. Finite Convergence of MRI Neural Network fc Linearly Separable Training Patterns [C] . Lijun Liu, Wei Wu International Symposium on Neural Networks pt.1; 20040819-20040821; Dalian; CN . 2004

机译：线性可分离训练模式的MRI神经网络的有限收敛性
5. Convergence analysis of a domain decomposition method for separable PDEs. [D] . Zhuang, Gongyuan. 2000

机译：可分离PDE的域分解方法的收敛性分析。
6. Deterministic convergence of chaos injection-based gradient method for training feedforward neural networks [O] . Huisheng Zhang, Ying Zhang, Dongpo Xu, 2015

机译：基于混沌注入的梯度法训练前馈神经网络的确定性收敛性
7. Convergence of online gradient methods for continuous perceptrons with linearly separable training patterns [O] . Wu Wei, Shao Zhiqiong 2003

机译：具有线性可分离训练模式的连续感知器在线梯度方法的收敛性

Convergence of online gradient methods for continuous perceptrons with linearly separable training patterns

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