Active learning for noisy oracle via density power divergence

Authors:
Yasuhiro Sogawa;Tsuyoshi Ueno;Yoshinobu Kawahara;Takashi Washio
Affiliations:
-;-;-;-
Venue:
Neural Networks
Year:
2013

Citing 11
Cited 0

Soft Margins for AdaBoost

Machine Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Query Learning with Large Margin Classifiers

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Improving Short-Text Classification using Unlabeled Data for Classification Problems

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Support vector machine active learning with applications to text classification

The Journal of Machine Learning Research
Batch mode active learning and its application to medical image classification

ICML '06 Proceedings of the 23rd international conference on Machine learning
Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error

The Journal of Machine Learning Research
Robust parameter estimation with a small bias against heavy contamination

Journal of Multivariate Analysis
A Least-squares Approach to Direct Importance Estimation

The Journal of Machine Learning Research
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)

Quantified Score

Hi-index	0.00

Visualization

Abstract

The accuracy of active learning is critically influenced by the existence of noisy labels given by a noisy oracle. In this paper, we propose a novel pool-based active learning framework through robust measures based on density power divergence. By minimizing density power divergence, such as @b-divergence and @c-divergence, one can estimate the model accurately even under the existence of noisy labels within data. Accordingly, we develop query selecting measures for pool-based active learning using these divergences. In addition, we propose an evaluation scheme for these measures based on asymptotic statistical analyses, which enables us to perform active learning by evaluating an estimation error directly. Experiments with benchmark datasets and real-world image datasets show that our active learning scheme performs better than several baseline methods.