文章基本信息

标题：On-line Learning with Malicious Noise and the Closure Algorithm
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作者：Peter Auer ; Nicolo Cesa-Bianchi
期刊名称：Electronic Colloquium on Computational Complexity
印刷版ISSN：1433-8092
出版年度：2000
卷号：2000
出版社：Universität Trier, Lehrstuhl für Theoretische Computer-Forschung
摘要：We investigate a variant of the on-line learning model for classes of {0,1}-valued functions (concepts) in which the labels of a certain amount of the input instances are corrupted by adversarial noise. We propose an extension of a general learning strategy, known as "Closure Algorithm", to this noise model, and show a worst-case mistake bound for learning an arbitrary intersection-closed concept class. For several concept classes our extended Closure Algorithm is efficient and can tolerate a noise rate up to the information-theoretic upper bound. Finally, we show how to efficiently turn any algorithm for the on-line noise model into a learning algorithm for the PAC model with malicious noise.