An improved error-correcting output coding framework with kernel-based decoding

An improved error-correcting output coding framework with kernel-based decoding
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一种基于内核解码的改进纠错输出编码框架

DOI:
10.1016/j.neucom.2008.04.032
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发表时间:
2008-10
期刊:
影响因子:
6
通讯作者:
Xiong, Rong
Xiong, Rong
中科院分区:
计算机科学2区
文献类型:
--
作者:
Luo, Dijun;Xiong, Rong

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本文提出了一种新颖的纠错输出编码(ECOC)框架来解决多类分类问题。通过对独立训练的每个基分类器的输出空间进行加权,调整解码的距离函数,使得样本更具辨别力。提出了在扩展对样本(EPS)上生成的标准来训练输出空间的权重。我们首先对 UCI 数据集进行实证研究,用四个常用的编码矩阵验证所提出的框架,然后将其应用于 RoboCup 领域以提高智能体控制的性能。本文还测试了基于颜色的图像分割。实验结果表明,我们的监督学习解码方案在从经验中学习后,显着提高了分类的准确性,并改善了足球比赛中智能体的控球能力。新框架中仍然保留一些属性:任何分类器以及距离函数仍然适用。此外,新框架允许使用内核技巧来实现非线性解码过程。
This paper presents a novel framework of error-correcting output coding (ECOC) addressing the problem of multi-class classification. By weighting the output space of each base classifier which is trained independently, the distance function of decoding is adapted so that the samples are more discriminative. A criterion generated over the Extended Pair Samples (EPS) is proposed to train the weights of output space. We first conduct empirical studies on UCI datasets to verify the presented framework with four frequently used coding matrixes and then apply it in RoboCup domain to enhance the performance of agent control. Color-based image segmentation is also tested in this paper. Experimental results show that our supervised learned decoding scheme improves the accuracy of classification significantly and betters the ball control of agents in a soccer game after learning from experience. Some properties still hold in the new framework: any classifier, as well as distance function, is still applicable. Besides, it is allowed in the new framework to implement nonlinear decoding processes using kernel tricks.
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