New bounds on classification error rates for the error-correcting output code (ECOC) approach in machine learning are presented. These bounds have exponential decay complexity with respect to codeword length and theoretically validate the effectiveness of the ECOC approach. Bounds are derived for two different models: the first under the assumption that all base classifiers are independent and the second under the assumption that all base classifiers are mutually correlated up to first-order. Moreover, we perform ECOC classification on six datasets and compare their error rates with our bounds to experimentally validate our work and show the effect of correlation on classification accuracy.

翻译：介绍了机器学习中错误更正产出代码(ECOC)方法的分类错误率的新界限,这些界限在编码长度方面具有指数衰变复杂性,理论上验证了ECOC方法的有效性,为两种不同的模型得出了曲线:第一个模型假设所有基级分类员都是独立的,第二个模型假设所有基级分类员在一阶前是相互联系的。此外,我们对所有基级分类员进行了六个数据集的分类,并将其误差率与我们实验性验证我们工作的界限进行比较,并显示对分类准确性的相关影响。