Apparatus and method for learning a model corresponding to time-series input data
Abstract:
A learning apparatus and method for learning a model corresponding to time-series input data, comprising: acquire the time-series input data; supply a plurality of input nodes of the model with a plurality of input values corresponding to input data at one time point in the time-series input data; store values of hidden nodes; compute a conditional probability of each input value at the one time point on a condition that an input data sequence has occurred, based on the input data sequence before the one time point in the time-series input data, on the stored values of hidden nodes, and on weight parameters; and perform a learning process that further increases a conditional probability of input data occurring at the one time point on the condition that the input data sequence has occurred, by adjusting the weight parameters.
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