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Discriminative probabilistic prototype learning

Bonilla, Edwin; Robles-Kelly, Antonio


In this paper we propose a simple yet powerful method for learning representations in supervised learning scenarios where an input datapoint is described by a set of feature vectors and its associated output may be given by soft labels indicating, for example, class probabilities. We represent an input datapoint as a K-dimensional vector, where each component is a mixture of probabilities over its corresponding set of feature vectors. Each probability indicates how likely a feature vector is to...[Show more]

CollectionsANU Research Publications
Date published: 2012
Type: Conference paper
Source: Proceedings of the 29th International Conference on Machine Learning, ICML 2012


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