SlideWiki | undefined | Active Learning

Small screen detected. You are viewing the mobile version of SlideWiki. If you wish to edit slides you will need to use a larger device.

Active Learning

Class labels are expensive to obtain
Active learner: query human (oracle) for labels
Pool-based approach: Uses a pool of unlabeled data

L: a small subset of D is labeled, U: a pool of unlabeled data in D
Use a query function to carefully select one or more tuples from U and request labels from an oracle (a human annotator)
The newly labeled samples are added to L, and learn a model
Goal: Achieve high accuracy using as few labeled data as possible

Evaluated using learning curves: Accuracy as a function of the number of instances queried (# of tuples to be queried should be small)
Research issue: How to choose the data tuples to be queried?

Uncertainty sampling: choose the least certain ones
Reduce version space, the subset of hypotheses consistent w. the training data
Reduce expected entropy over U: Find the greatest reduction in the total number of incorrect predictions

Speaker notes:

There are currently no sources for this slide.