US 2026/0094066 A1 · METHODS AND SYSTEMS FOR ACTIVE MACHINE LEARNING

Abstract

Methods and systems are described for selecting a group of data points to be labelled in an active learning process. The method comprises the step of determining a temperature parameter that selects between a representation-based point selection method and an uncertainty-based point selection method based on the current budget. The method also comprises the step of determining a kernel radius parameter that is inversely proportional to the size of the current budget. The method then includes the step of estimating an uncertainty coverage based on the temperature parameter, the kernel radius parameter and the current budget, wherein the uncertainty coverage is a measure of how much uncertainty is covered by the group of data points. Finally, the next point is greedily selected based on the estimated uncertainty coverage.

Claims11

Dependent claims

A method for selecting a group of data points to be labelled in an active learning process, the method comprising:

determining a temperature parameter that selects between a representation-based point selection method and an uncertainty-based point selection method based on the current budget;
determining a kernel radius parameter that is inversely proportional to the size of the current budget;
estimating an uncertainty coverage based on the temperature parameter, the kernel radius parameter and the current budget, wherein the uncertainty coverage is a measure of how much uncertainty is covered by the group of data points;
and
greedily selecting a next point based on the estimated uncertainty coverage.

2 more elements

Dependencies are inferred from the claim text.

Methods and systems for active machine learning

Life of this patent

Methods and systems for active machine learning

Life of this patent

Abstract

Claims11