Hyperparameter and Kernel Learning for Graph Based Semi-Supervised Classification
Microsoft
Hyperparameter and Kernel Learning for Graph Based Semi-Supervised Classification
There have been many graph-based approaches for semi-supervised classification. One problem is that of hyperparameter learning: performance depends greatly on the hyperparameters of the similarity graph, transformation of the graph Laplacian and the noise model. We present a Bayesian framework for learning hyperparameters for graph-based semi ...
Oct 31, 2009
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