Crossover Random Fields
نویسنده
چکیده
Conditional Random Fields (CRFs) encounter computational difficulties in the presence of a graph with high treewidth. This paper introduces a new type of model, the Crossover Random Field (XRF), designed to cope with these difficulties. An XRF consists of a sequence of “layers”, each of which is a tractable CRF. Inference of marginals takes place layer by layer, with the marginals from one layer parameterizing the next. Learning is done not by the likelihood, but by use of a univariate loss function, directly measuring the accuracy of the marginals produced by the model.
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تاریخ انتشار 2008