Bayesian Active Learning for Classification and Preference Learning

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Information-based objective functions for active data selection

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Query by committee

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Toward optimal active learning through sampling estimation of error reduction

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Support vector machine active learning with applications to text classification

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Active Testing Surveillance Systems, or, Playing Twenty Questions with a Radar

Fuhrmann, D. (2003) · 2003

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Pairwise preference learning and ranking

Fürnkranz, J. and Hüllermeier, E. (2003) · 2003

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Fast sparse Gaussian Process methods: The informative vector machine

Lawrence, N., Seeger, M., and Herbrich, R. (2003) · 2003

Cited alongside, same era.

Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions

Zhu, X., Ghahramani, Z., and Lafferty, J. (2003) · 2003

Cited alongside, same era.

Preference learning with Gaussian processes

Chu, W. and Ghahramani, Z. (2005) · 2005

Assesing approximations for gaussian process classification

Kuss, M. and Rasmussen, C. E. (2005) · 2005

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Gaussian Processes for Machine Learning

Rasmussen, C. and Williams, C. (2005) · 2005

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Near-optimal sensor placements: Maximizing information while minimizing communication cost

Krause, A., Guestrin, C., Gupta, A., and Kleinberg, J. (2006) · 2006

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Selective supervision: Guiding supervised learning with decision-theoretic active learning

Kapoor, A., Horvitz, E., and Basu, S. (2007) · 2007

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Correcting for the sampling bias problem in spike train information measures

Panzeri, S., S. R. M. M. and Petersen, R. (2007) · 2007

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Adaptive submodularity: A new approach to active learning and stochastic optimization