Learning from Ambiguous Examples
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Learning From Ambiguous Examples
Current inductive learning systems are not well suited to learning from ambiguous examples. We say that an example is ambiguous if it has multiple interpretations, only one of which may be valid. Some domains in which ambiguous learning problems can be found are natural language processing (NLP) and computer vision. An example of an ambiguous training instance with two interpretations is shown ...
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Positive and unlabelled learning (PU learning) has been investigated to deal with the situation where only the positive examples and the unlabelled examples are available. Most of the previous works focus on identifying some negative examples from the unlabelled data, so that the supervised learning methods can be applied to build a classifier. However, for the remaining unlabelled data, which ...
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In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework for learning with ambiguous objects, where an example is described by multiple instances and associated with multiple class labels. Comparing with traditional learning frameworks, the MIML framework is more convenient and natural for representing ambiguous objects. To learn MIML examples, we propose the MimlBoost...
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We investigate here concept learning from incomplete examples, denoted here as ambiguous. We start from the learning from interpretations setting introduced by L. De Raedt and then follow the informal ideas presented by H. Hirsh to extend the Version space paradigm to incomplete data: a hypothesis has to be compatible with all pieces of information provided regarding the examples. We propose an...
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تاریخ انتشار 2005