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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >An algorithm to determine the feasibilities and weights of two-layer perceptrons for partitioning and classification
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An algorithm to determine the feasibilities and weights of two-layer perceptrons for partitioning and classification

机译:确定用于划分和分类的两层感知器的可行性和权重的算法

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摘要

Necessary and sufficient conditions for implementing classification problems by two-layer perceptrons have been presented in the viewpoints of mathematics and geometry. [Zwietering, Arts and Wessels, Int. J. Neural Systems 3(2), 143-156 (1992)]. This paper, in an engineering viewpoint, provides an algorithm, called the Weight Deletion/Selection Algorithm, to examine the feasibility of implementation of a decision region by two-layer perceptrons, and to select the weights of the second layer in a two-layer perceptron without any training process if a decision region is implementable for two-layer perceptrons. For the purpose of visualization, we explain the algorithm by the examples of two-input-two-class cases, and then discuss the generalization to multi-input-multi-class cases. Finally we present a three-class classification example with four features selected as the inputs. (C) 1998 Published by Elsevier Science Ltd on behalf of the Pattern Recognition Society. All rights reserved. [References: 19]
机译:从数学和几何学的观点出发,已经提出了通过两层感知器实现分类问题的充要条件。 [Zwietering,艺术与韦塞尔,国际J.Neural Systems 3(2),143-156(1992)]。本文从工程角度出发,提供了一种称为权重删除/选择算法的算法,以检查通过两层感知器实现决策区域的可行性,并选择两层感知器中第二层的权重如果决策区域可用于两层感知器,则无需任何训练过程即可获得感知器。出于可视化的目的,我们以两输入两类情况为例来解释该算法,然后讨论对多输入多类情况的推广。最后,我们给出一个三类分类示例,其中选择了四个特征作为输入。 (C)1998由Elsevier Science Ltd代表模式识别协会出版。版权所有。 [参考:19]

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