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Latent Variable Perceptron Algorithm for Structured Classification

2019-11-15 | |  61 |   41 |   0

Abstract We propose a perceptron-style algorithm for fast discriminative training of structured latent variable model, and analyzed its convergence properties. Our method extends the perceptron algorithm for the learning task with latent dependencies, which may not be captured by traditional models. It relies on Viterbi decoding over latent variables, combined with simple additive updates. Compared to existing probabilistic models of latent variables, our method lowers the training cost signifificantly yet with comparable or even superior classifification accuracy

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