学习基准测试：从训练数据中确定最佳可消除的误分类错误
Learning to Benchmark: Determining Best Achievable Misclassification Error from Training Data
摘要：
我们解决的问题，学习基准的最佳实现分类器的性能。在这个问题中，目标是建立统计上相合估计的贝叶斯误分类错误率，而不必学习贝叶斯最优分类器。我们的学习基准框架改进了以前学习贝叶斯误分类率界限的工作，因为它学习{ \it exact}贝叶斯错误率而不是错误率界限。我们提出了一个基准学习的基础上合奏的$ε$球估计和切比雪夫近似。在类密度的平滑假设下，我们证明了我们的估计达到了最佳（参数）均方误差（MSE）率为$O(N^{-1})$，其中$N$是样本数。在模拟和真实数据集上的实验表明，我们提出的基准学习算法产生的贝叶斯误差估计比以前的贝叶斯误差概率学习方法更准确。
英文摘要：
We address the problem of learning to benchmark the best achievable classifier performance. In this problem the objective is to establish statistically consistent estimates of the Bayes misclassification error rate without having to learn a Bayes-optimal classifier. Our learning to benchmark framework improves on previous work on learning bounds on Bayes misclassification rate since it learns the {\it exact} Bayes error rate instead of a bound on error rate. We propose a benchmark learner based on an ensemble of $ε$-ball estimators and Chebyshev approximation. Under a smoothness assumption on the class densities we show that our estimator achieves an optimal (parametric) mean squared error (MSE) rate of $O(N^{-1})$, where $N$ is the number of samples. Experiments on both simulated and real datasets establish that our proposed benchmark learning algorithm produces estimates of the Bayes error that are more accurate than previous approaches for learning bounds on Bayes error probability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
