Patent ID: 8566260
Filing Date: 2013-10-22
Classification: G06N

Abstract:
1. A structured prediction model learning apparatus, having a central processing unit, for learning a structured prediction model used to predict an output structure y corresponding to an input structure x, by using supervised data D L and unsupervised data D U , the structured prediction model learning apparatus comprising: an output candidate graph generator implemented by the central processing unit to generate a supervised data output candidate graph for the supervised data and an unsupervised data output candidate graph for the unsupervised data, by using a set of definition data for generating output candidates identified by a structured prediction problem; a feature vector generator extracting features from the supervised data output candidate graph and the unsupervised data output candidate graph by using a feature extraction template, generating a D-dimensional base-model feature vector f a parameter generator generating a base-model parameter set λ which includes a first parameter set w formed of D first parameters in one-to-one correspondence with D elements of the base-model feature vector f an auxiliary model parameter estimating unit estimating the set Θ of auxiliary model parameter sets which minimizes the Bregman divergence having a regularization term obtained from the auxiliary model parameter set θ a base-model parameter estimating unit estimating a base-model parameter set λ which minimizes an empirical risk function defined beforehand, by using the supervised data D wherein the auxiliary model parameter estimating unit uses the auxiliary model parameter set θ where C u is a hyper parameter and Ĝ D ({tilde over (r)}∥q k ) is a generalized relative entropy obtained by using the unsupervised data D U , and estimates the set Θ of auxiliary model parameter sets which minimizes the empirical generalized relative entropy having the regularization term.