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PKDD
2010
Springer
164views Data Mining» more  PKDD 2010»
15 years 4 months ago
Complexity Bounds for Batch Active Learning in Classification
Active learning [1] is a branch of Machine Learning in which the learning algorithm, instead of being directly provided with pairs of problem instances and their solutions (their l...
Philippe Rolet, Olivier Teytaud
ECIR
2011
Springer
14 years 10 months ago
Balancing Exploration and Exploitation in Learning to Rank Online
Abstract. As retrieval systems become more complex, learning to rank approaches are being developed to automatically tune their parameters. Using online learning to rank approaches...
Katja Hofmann, Shimon Whiteson, Maarten de Rijke
EUROCOLT
1999
Springer
15 years 10 months ago
Regularized Principal Manifolds
Many settings of unsupervised learning can be viewed as quantization problems — the minimization of the expected quantization error subject to some restrictions. This allows the ...
Alex J. Smola, Robert C. Williamson, Sebastian Mik...
ICML
2000
IEEE
16 years 7 months ago
Less is More: Active Learning with Support Vector Machines
We describe a simple active learning heuristic which greatly enhances the generalization behavior of support vector machines (SVMs) on several practical document classification ta...
Greg Schohn, David Cohn
SAC
2009
ACM
16 years 1 months ago
Evaluating algorithms that learn from data streams
In the past years, the theory and practice of machine learning and data mining have been focused on static and finite data sets from where learning algorithms generate a static m...
João Gama, Pedro Pereira Rodrigues, Raquel ...