Exponential family graph matching and ranking
We present a method for learning max-weight matching predictors in bipartite graphs. The method consists of performing maximum a posteriori estimation in exponential families with sufficient statistics that encode permutations and data features. Although
|Collections||ANU Research Publications|
|Source:||Proceedings of The 23rd Annual Conference on Neural Information Processing Systems (NIPS 23)|
|01_Petterson_Exponential_family_graph_2009.pdf||638.52 kB||Adobe PDF||Request a copy|
|02_Petterson_Exponential_family_graph_2009.pdf||17.51 kB||Adobe PDF||Request a copy|
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