Interactive Learning from Multiple Noisy Labels
Abstract
Interactive learning is a process in which a machine learning algorithm is provided with meaningful, well-chosen examples as opposed to randomly chosen examples typical in standard supervised learning. In this paper, we propose a new method for interactive learning from multiple noisy labels where we exploit the disagreement among annotators to quantify the easiness or meaningfulness of an example. We demonstrate the usefulness of this method in estimating the parameters of a latent variable classification model, and conduct experimental analyses on a range of synthetic and benchmark datasets. Furthermore, we theoretically analyze the performance of perceptron in this interactive learning framework.
Cite
Text
Vembu and Zilles. "Interactive Learning from Multiple Noisy Labels." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46128-1_31Markdown
[Vembu and Zilles. "Interactive Learning from Multiple Noisy Labels." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/vembu2016ecmlpkdd-interactive/) doi:10.1007/978-3-319-46128-1_31BibTeX
@inproceedings{vembu2016ecmlpkdd-interactive,
title = {{Interactive Learning from Multiple Noisy Labels}},
author = {Vembu, Shankar and Zilles, Sandra},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2016},
pages = {493-508},
doi = {10.1007/978-3-319-46128-1_31},
url = {https://mlanthology.org/ecmlpkdd/2016/vembu2016ecmlpkdd-interactive/}
}