New results on error correcting output codes of kernel machines
New results on error correcting output codes of kernel machines
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DOI：
10.1109/tnn.2003.820841
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发表时间：
2004
影响因子：
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通讯作者：
Andrea Passerini;M. Pontil;P. Frasconi
中科院分区：
文献类型：
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作者：
Andrea Passerini;M. Pontil;P. Frasconi
我们研究的问题，多类分类的纠错输出代码（ECOC）的框架内使用基于保证金的二进制分类。具体来说，我们在这种情况下解决两个重要的开放问题：解码和模型选择。解码问题涉及如何将分类器的输出映射成类码字。在本文中，我们引入了一个新的解码功能，通过估计他们的类条件概率的利润率相结合。关于模型选择，我们提出了新的理论结果，限制了核机器的ECOC的留一法（LOO）误差，可用于调整核超参数。我们报告使用支持向量机作为基本的二进制分类器的实验，显示出所提出的解码功能的优势，在实践中常用的其他功能的I他的保证金。此外，我们对模型选择的经验评估表明，该界导致核参数的良好估计。
We study the problem of multiclass classification within the framework of error correcting output codes (ECOC) using margin-based binary classifiers. Specifically, we address two important open problems in this context: decoding and model selection. The decoding problem concerns how to map the outputs of the classifiers into class codewords. In this paper we introduce a new decoding function that combines the margins through an estimate of their class conditional probabilities. Concerning model selection, we present new theoretical results bounding the leave-one-out (LOO) error of ECOC of kernel machines, which can be used to tune kernel hyperparameters. We report experiments using support vector machines as the base binary classifiers, showing the advantage of the proposed decoding function over other functions of I he margin commonly used in practice. Moreover, our empirical evaluations on model selection indicate that the bound leads to good estimates of kernel parameters.