منابع مشابه
Semi-Supervised Novelty Detection
A common setting for novelty detection assumes that labeled examples from the nominal class are available, but that labeled examples of novelties are unavailable. The standard (inductive) approach is to declare novelties where the nominal density is low, which reduces the problem to density level set estimation. In this paper, we consider the setting where an unlabeled and possibly contaminated...
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Michael I. Jordan Computer Science and Statistics, V.C. Berkeley jordan@cs. berkeley. edu In this paper we consider the problem of novelty detection, presenting an algorithm that aims to find a minimal region in input space containing a fraction 0: of the probability mass underlying a data set. This algorithmthe "single-class minimax probability machine (MPM)" is built on a distribution-free me...
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Most semi-supervised learning algorithms have been designed for binary classification, and are extended to multi-class classification by approaches such as one-against-the-rest. The main shortcoming of these approaches is that they are unable to exploit the fact that each example is only assigned to one class. Additional problems with extending semisupervised binary classifiers to multi-class p...
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ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2014
ISSN: 0162-8828,2160-9292
DOI: 10.1109/tpami.2014.2327984