Learning from binary labels with instance-dependent noise
نویسندگان
چکیده
منابع مشابه
Learning from Binary Labels with Instance-Dependent Corruption
Suppose we have a sample of instances paired with binary labels corrupted by arbitrary instanceand label-dependent noise. With sufficiently many such samples, can we optimally classify and rank instances with respect to the noise-free distribution? We provide a theoretical analysis of this question, with three main contributions. First, we prove that for instance-dependent noise, any algorithm ...
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Instanceand label-dependent label noise (ILN) is widely existed in real-world datasets but has been rarely studied. In this paper, we focus on a particular case of ILN where the label noise rates, representing the probabilities that the true labels of examples flip into the corrupted labels, have upper bounds. We propose to handle this bounded instanceand label-dependent label noise under two d...
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متن کاملLearning from Corrupted Binary Labels via Class-Probability Estimation
Many supervised learning problems involve learning from samples whose labels are corrupted in some way. For example, each label may be flipped with some constant probability (learning with label noise), or one may have a pool of unlabelled samples in lieu of negative samples (learning from positive and unlabelled data). This paper uses class-probability estimation to study these and other corru...
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In recommender systems, user ratings of items are often represented in terms of linguistic labels such as “fair” or “very good”. We investigate the potential of fuzzy sets as a means for modeling such labels, especially with regard to collaborative filtering algorithms. We propose a related fuzzy version of instance-based (memory-based) collaborative filtering and argue that it leads to more in...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 2018
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-018-5715-3