2016
DOI: 10.1109/tnnls.2015.2497275
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Learning in Variable-Dimensional Spaces

Abstract: This paper proposes a unified approach to learning in environments in which patterns can be represented in variable-dimension domains, which nicely includes the case in which there are missing features. The proposal is based on the representation of the environment by pointwise constraints that are shown to model naturally pattern relationships that come out in problems of information retrieval, computer vision, and related fields. The given interpretation of learning leads to capturing the truly different asp… Show more

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Cited by 2 publications
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