Discriminative probabilistic prototype learning
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]
|Collections||ANU Research Publications|
|Source:||Proceedings of the 29th International Conference on Machine Learning, ICML 2012|
|01_Bonilla_Discriminative_probabilistic_2012.pdf||350.07 kB||Adobe PDF||Request a copy|
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