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2005
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Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning

9 years 5 months ago
Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning
Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert). Many conventional active learning algorithms focus on refining the decision boundary, at the expense of exploring new regions that the current hypothesis misclassifies. We propose a new active learning algorithm that balances such exploration with refining of the decision boundary by dynamically adjusting the probability to explore at each step. Our experimental results demonstrate improved performance on data sets that require extensive exploration while remaining competitive on data sets that do not. Our algorithm also shows significant tolerance of noise.
Thomas Takeo Osugi, Kun Deng, Stephen D. Scott
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where ICDM
Authors Thomas Takeo Osugi, Kun Deng, Stephen D. Scott
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