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Inducing Features of Random Fields Export

IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 4. (1997), pp. 380-393.

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We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the Kullback-Leibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the field and an iterative...


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