Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Efficient Interactive Multiclass Learning from Binary Feedback

Efficient Interactive Multiclass Learning from Binary Feedback Efficient Interactive Multiclass Learning from Binary Feedback HUNG NGO, MATTHEW LUCIW, JAWAS NAGI, ALEXANDER FORSTER, ¨ and JURGEN SCHMIDHUBER, IDSIA, Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Manno-Lugano, Switzerland NGO ANH VIEN, MLR Lab, University of Stuttgart, Germany We introduce a novel algorithm called upper confidence-weighted learning (UCWL) for online multiclass learning from binary feedback (e.g., feedback that indicates whether the prediction was right or wrong). UCWL combines the upper confidence bound (UCB) framework with the soft confidence-weighted (SCW) online learning scheme. In UCB, each instance is classified using both score and uncertainty. For a given instance in the sequence, the algorithm might guess its class label primarily to reduce the class uncertainty. This is a form of informed exploration, which enables the performance to improve with lower sample complexity compared to the case without exploration. Combining UCB with SCW leads to the ability to deal well with noisy and nonseparable data, and state-of-the-art performance is achieved without increasing the computational cost. A potential application setting is human-robot interaction (HRI), where the robot is learning to classify some set of inputs while the human teaches it by providing only binary feedback--or sometimes even the wrong answer entirely. Experimental http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Interactive Intelligent Systems (TiiS) Association for Computing Machinery

Loading next page...
 
/lp/association-for-computing-machinery/efficient-interactive-multiclass-learning-from-binary-feedback-87aJ6Jqqp6
Publisher
Association for Computing Machinery
Copyright
Copyright © 2014 by ACM Inc.
ISSN
2160-6455
DOI
10.1145/2629631
Publisher site
See Article on Publisher Site

Abstract

Efficient Interactive Multiclass Learning from Binary Feedback HUNG NGO, MATTHEW LUCIW, JAWAS NAGI, ALEXANDER FORSTER, ¨ and JURGEN SCHMIDHUBER, IDSIA, Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, Manno-Lugano, Switzerland NGO ANH VIEN, MLR Lab, University of Stuttgart, Germany We introduce a novel algorithm called upper confidence-weighted learning (UCWL) for online multiclass learning from binary feedback (e.g., feedback that indicates whether the prediction was right or wrong). UCWL combines the upper confidence bound (UCB) framework with the soft confidence-weighted (SCW) online learning scheme. In UCB, each instance is classified using both score and uncertainty. For a given instance in the sequence, the algorithm might guess its class label primarily to reduce the class uncertainty. This is a form of informed exploration, which enables the performance to improve with lower sample complexity compared to the case without exploration. Combining UCB with SCW leads to the ability to deal well with noisy and nonseparable data, and state-of-the-art performance is achieved without increasing the computational cost. A potential application setting is human-robot interaction (HRI), where the robot is learning to classify some set of inputs while the human teaches it by providing only binary feedback--or sometimes even the wrong answer entirely. Experimental

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS)Association for Computing Machinery

Published: Aug 1, 2014

There are no references for this article.