Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.568291
Title: Learning from interaction : models and applications
Author: Glowacka, D.
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2012
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Abstract:
A large proportion of Machine Learning (ML) research focuses on designing algorithms that require minimal input from the human. However, ML algo- rithms are now widely used in various areas of engineering to design and build systems that interact with the human user and thus need to “learn” from this interaction. In this work, we concentrate on algorithms that learn from user interaction. A significant part of the dissertation is devoted to learning in the bandit setting. We propose a general framework for handling dependencies across arms, based on the new assumption that the mean-reward function is drawn from a Gaussian Process. Additionally, we propose an alternative method for arm selection using Thompson sampling and we apply the new algorithms to a grammar learning problem. In the remainder of the dissertation, we consider content-based image re- trieval in the case when the user is unable to specify the required content through tags or other image properties and so the system must extract infor- mation from the user through limited feedback. We present a novel Bayesian approach that uses latent random variables to model the systems imperfect knowledge about the users expected response to the images. An impor- tant aspect of the algorithm is the incorporation of an explicit exploration- exploitation strategy in the image sampling process. A second aspect of our algorithm is the way in which its knowledge of the target image is updated given user feedback. We considered a few algorithms to do so: variational Bayes, Gibbs sampling and a simple uniform update. We show in experi- ments that the simple uniform update performs best. The reason is because, unlike the uniform update, both variational Bayes and Gibbs sampling tend to focus on a small set of images aggressively.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.568291  DOI: Not available
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