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Unsupervised learning

Unsupervised learning

This video was recorded at Summer Schools in Logic and Learning, Canberra 2009. The first part of his tutorial will discuss un-supervised, semi-supervised and partially-supervised learning. Convex relaxations will be presented for un-supervised and semi-supervised training of support vector machines, max-margin Markov networks, log-linear models and Bayesian networks. The concept of partially-supervised training will then be introduced, with convex relaxations developed for training multi-layer perceptrons and deep networks. Relationships of these methods to classical training algorithms (EM, Viterbi-EM, and self-supervised training) will be discussed. Limitations of convex relaxations will also be considered. The tutorial will then present methods for scaling up such training algorithms.... Show More


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