Learning in the presence of malicious errors
Learning in the presence of malicious errors
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DOI：
10.1145/62212.62238
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发表时间：
1993-08
期刊：
影响因子：
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通讯作者：
M. Kearns;Ming Li
中科院分区：
文献类型：
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作者：
M. Kearns;Ming Li
在本文中，Valiant引入的无分配模型[Comm。这种错误是通过无限制的计算能力的广告产生的，并且对学习算法的整个历史记录进行了访问，该算法的计算是最坏的错误模型。通过任何学习算法，可容忍恶意错误率的有效算法，以及与错误的学习问题与标准组合优化问题之间的等价。
In this paper an extension of the distribution-free model of learning introduced by Valiant [Comm. ACM, 27(1984), pp. 1134–1142] that allows the presence of malicious errors in the examples given to a learning algorithm is studied. Such errors are generated by an adversary with unbounded computational power and access to the entire history of the learning algorithm’s computation. Thus, a worst-case model of errors is studied.The results of this research include general methods for bounding the rate of error tolerable by any learning algorithm, efficient algorithms tolerating nontrivial rates of malicious errors, and equivalences between problems of learning with errors and standard combinatorial optimization problems.