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Coding design for error correcting output codes based on perceptron

机译：基于感知机的纠错输出码编码设计

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摘要

It is known that error-correcting output codes (ECOC) is a common way to model multiclass classification problems, in which the research of encoding based on data is attracting more and more attention. We propose a method for learning ECOC with the help of a single-layered perception neural network. To achieve this goal, the code elements of ECOC are mapped to the weights of network for the given decoding strategy, and an object function with the constrained weights is used as a cost function of network. After the training, we can obtain a coding matrix including lots of subgroups of class. Experimental results on artificial data and University of California Irvine with logistic linear classifier and support vector machine as the binary learner show that our scheme provides better performance of classification with shorter length of coding matrix than other state-of-the-art encoding strategies.
机译：众所周知，纠错输出码（ECOC）是多类分类问题建模的常用方法，其中基于数据的编码研究越来越受到关注。我们提出了一种借助单层感知神经网络学习ECOC的方法。为了实现该目标，将ECOC的代码元素映射到给定解码策略的网络权重上，并将具有约束权重的对象函数作为网络的代价函数。训练后，我们可以得到一个编码矩阵，其中包含许多类的子组。在人工数据和加州大学欧文分校以逻辑线性分类器和支持向量机为二元学习器的实验结果表明，与其他先进的编码策略相比，该方案在编码矩阵长度更短的情况下提供了更好的分类性能。

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