Active Learning Method for Multi-Class Classifiers
AssigneeMitsubishi Electric Research Laboratories, Inc.
InventorsFatih M. Porikli, Ajay Joshi
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Abstract
A method trains a multi-class classifier by iteratively performing the following steps until a termination condition is reached. The probabilities of class membership for unlabeled data obtained from an active pool of unlabeled data are estimated. A difference between a largest probability and a second largest probability is determined. The unlabeled data with the lowest difference is selected, labeled and then added to a training data set for training the classifier.
Claims20
A method for training a multi-class classifier, comprising a processor for performing steps of the method iteratively until a termination condition is reached, comprising the steps of:
- estimating probabilities of class membership for unlabeled data obtained from an active pool of unlabeled data;
- determining a difference between a largest probability and a second largest probability;
- selecting the unlabeled data with the lowest difference;
- labeling the selected unlabeled data;
- adding the labeled data to a training data set;
- and
- training the classifier with the training data set.
Dependencies are inferred from the claim text.