Distributed hyperparameter tuning system for machine learning
US10360517B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 27, 2017 |
| Grant date | Jul 23, 2019 |
| Priority date | — |
| Expiry date | Jan 7, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computing device automatically selects hyperparameter values based on objective criteria to train a predictive model. Each session of a plurality of sessions executes training and scoring of a model type using an input dataset in parallel with other sessions of the plurality of sessions. Unique hyperparameter configurations are determined using a search method and assigned to each session. For each session of the plurality of sessions, training of a model of the model type is requested using a training dataset and the assigned hyperparameter configuration, scoring of the trained model using a validation dataset and the assigned hyperparameter configuration is requested to compute an objective function value, and the received objective function value and the assigned hyperparameter configuration are stored. A best hyperparameter configuration is identified based on an extreme value of the stored objective function values.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.