Patent ID: 11868440
Assignee: A9.COM, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 9:
10. A system, comprising:
at least one processor; and
a memory including instructions that, when executed by the at least one processor, cause the system to:
obtain a set of training data for training a model, the training including multiple iterations, the set of training data including data instances each having a specified classification;
select, for a respective iteration of the training, a subset of the training data to use for the training, selection probabilities for instances of the set of training data being higher for instances having lower accuracies of classification as determined using a set of loss gradients for the instances of the set of training data, the subset of the training data selected using a probabilistic sampling algorithm accepting the loss gradients for the data instances of the set, the loss gradients derived from loss values representing a measure of inaccuracies of classification for the data instances;
train the model using respective subsets of training data over the multiple iterations to generate a trained model, weightings of instances of the respective subsets determined using respective loss gradients from the set of loss gradients;
applying regularization during the training to avoid overfitting of the model to the set of training data,
evaluate the trained model using a subset of evaluation data, selected from the set of training data, that was not included in the respective subsets used for the training; and
provide the trained model for use in classifying unclassified data.