多层感知器处理多分类问题的计算能力
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论文类型：期刊论文
发表时间：2022-06-29
发表刊物：Numerical Mathematics A Journal of Chinese University
卷号：42
期号：3
页面范围：277-288
ISSN号：1000-081X
关键字："Multiple linear perceptron; multi-class classification problem; one-to-one approach; binary-coding approach; accuracies"
CN号：32-1170/O1
摘要：In this paper, we consider the design of output layer nodes of multiple linear perceptron network for solving $r$-class classification problems $\left( {r \ge 3} \right)$. In general, the output layer is designed in an one-to-one" approach. Instead, we will adopt a "binary-coding" approach to build the output layer, which contains $q$ nodes such that ${2^{q - 1}} < r \le {2^q}$ with $q \ge 2$ and outputs a binary code of the number i if an input belongs to the i-th class. In particular, for multiple linear perceptrons with four hidden nodes, we prove the following result: One-to-one approach can solve an r-class classification problem with $r \le 16$ by using r output nodes, while our binary-coding approach can solve the same problem by using $q\left( {q \le 4} \right)$ output nodes.
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发表时间：2022-06-29