US 2019/0197013 A1 · PARALLELIZED BLOCK COORDINATE DESCENT FOR MACHINE LEARNED MODELS

Abstract

Iterations of a machine learned model training process are performed until a convergence occurs. A fixed effects machine learned model is trained using a first machine learning algorithm. Residuals of the training of the fixed effects machine learned model are determined by comparing results of the trained fixed effects machine learned model to a first set of target results. A first random effects machine learned model is trained using a second machine learning algorithm and the residuals of the training of the fixed effects machine learned model. Residuals of the training of the first random effect machine learned model are determined by comparing results of the trained first random effects machine learned model to a second set of target result, in each subsequent iteration the training of the fixed effects machine learned model uses residuals of the training of a last machine learned model trained in a previous iteration.

Claims20

Dependent claims

A system comprising:

a computer-readable medium having Instructions stored thereon, which, when executed by a processor, cause the system to:
perform one or more iterations of a machine learned model training process, the one or more iterations combining until a convergence test is met, each iteration comprising:
training a fixed effects machine learned model using a first machine learning algorithm:
determining residuals of the training of the fixed effects machine learned model by comparing results of the trained fixed effects machine learned model to a first set of target results;
training a first random effects machine learned model using a second machine learning algorithm and the residuals of the training of the fixed effects machine learned model:
and
determining residuals of the training of the first random effect machine learned model by comparing results of the trained first random effects machine learned model to a second set of target results;
and
wherein in each subsequent iteration the training of the fixed effects machine learned model uses residuals of the training of a last machine learned model trained in a previous iteration.

6 more elements

A non-transitory machine-readable storage medium comprising instructions which, when implemented by one or more machines, cause the one or more machines to perform operations comprising:

performing one or more iterations of a machine learned model training process, the one or more iterations continuing until a convergence test is met, each iteration comprising:
training a fixed effects machine learned model using a first machine learning algorithm;
determining residuals of the training of the fixed effects machine learned model by comparing results of the trained fixed effects machine learned model to a first set of target results;
training a first random effects machine learned model using a second machine learning algorithm and the residuals of the training of the fixed effects machine learned model;
and
determining residuals of the training of the first random effect machine learned model by comparing results of the trained first random effects machine learned model to a second set of target results;
and
wherein in each subsequent iteration the training of the fixed effects machine learned model uses residuals of the training of a last machine learned model trained in a previous iteration.

5 more elements

Dependencies are inferred from the claim text.

Parallelized block coordinate descent for machine learned models

Life of this patent

Parallelized block coordinate descent for machine learned models

Life of this patent

Abstract

Claims20