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Template regularization for generalization of learning systems

  • US 9,390,382 B2
  • Filed: 12/30/2013
  • Issued: 07/12/2016
  • Est. Priority Date: 12/30/2013
  • Status: Active Grant
First Claim
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1. A computer-implemented method of training a machine learning model on labeled examples, wherein the machine learning model is configured to receive an example having a plurality of features and to generate a predicted output for the received example, the method comprising:

  • obtaining data defining a plurality of templates, wherein each template corresponds to one or more categories of features;

    assigning a respective regularization penalty to each of the plurality of templates; and

    training the machine learning model on the labeled examples, comprising, for each labeled example and for each of the plurality of templates;

    determining, using the machine learning model, a respective weight for the template based on the features of the labeled example that belong to the one or more categories that correspond to the template, andmodifying the respective weight for the template by applying the respective regularization penalty for the template to the respective weight for the template determined by the machine learning model,wherein, during the training, a template having a lower regularization penalty is emphasized over a template having a higher regularization penalty.

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