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2013 ICML ICML 2013

Efficient Semi-supervised and Active Learning of Disjunctions

Abstract

We provide efficient algorithms for learning disjunctions in the semi-supervised setting under a natural regularity assumption introduced by (Balcan & Blum, 2005). We prove bounds on the sample complexity of our algorithms under a mild restriction on the data distribution. We also give an active learning algorithm with improved sample complexity and extend all our algorithms to the random classification noise setting.

🚀 Conference Pioneer — ICML 2013
🧭 Keyword Pioneer — learning disjunction
🐣 Hot Topic Early Bird — active learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Nina Balcan , Christopher Berlind , Steven Ehrlich , Yingyu Liang

Topics

Machine Learning > Learning Types > Active Learning Machine Learning > Learning Types > Semi-Supervised Learning Machine Learning > Learning Types > Classification Machine Learning > Learning Paradigms > Semi-Supervised Learning

Keywords

active learning semi-supervised learning sample complexity classification noise learning disjunction
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