Tuning hyper-parameters of a computer-executable learning algorithm
US9330362B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 15, 2013 |
| Grant date | May 3, 2016 |
| Priority date | — |
| Expiry date | Oct 30, 2033 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Technologies pertaining to tuning a hyper-parameter configuration of a learning algorithm are described. The learning algorithm learns parameters of a predictive model based upon the hyper-parameter configuration. Candidate hyper-parameter configurations are identified, and statistical hypothesis tests are undertaken over respective pairs of candidate hyper-parameter configurations to identify, for each pair of candidate hyper-parameter configurations, which of the two configurations is associated with better predictive performance. The technologies described herein take into consideration the stochastic nature of training data, validation data, and evaluation functions.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.