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Gaussian Clusters and Noise: An Approach Based on the Minimum Description Length Principle.

, , and . Discovery Science, volume 6332 of Lecture Notes in Computer Science, page 251-265. Springer, (2010)

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Gaussian Clusters and Noise: An Approach Based on the Minimum Description Length Principle., , and . Discovery Science, volume 6332 of Lecture Notes in Computer Science, page 251-265. Springer, (2010)The Power of Sampling in Knowledge Discovery., and . PODS, page 77-85. ACM Press, (1994)Learning Hierarchical Rule Sets., , and . COLT, page 37-44. ACM, (1992)Online Learning of Linear Classifiers.. Machine Learning Summer School, volume 2600 of Lecture Notes in Computer Science, page 235-258. Springer, (2002)Attribute-Efficient Learning.. Encyclopedia of Algorithms, Springer, (2008)Worst-case Loss Bounds for Single Neurons., , and . NIPS, page 309-315. MIT Press, (1995)Using experts for predicting continuous outcomes., and . EuroCOLT, page 109-120. Oxford University Press, (1993)Averaging Expert Predictions, and . EuroCOLT, volume 1572 of Lecture Notes in Computer Science, page 153-167. Springer, (1999)Exponentiated Gradient Versus Gradient Descent for Linear Predictors., and . Inf. Comput., 132 (1): 1-63 (1997)Approximate Dependency Inference from Relations., and . ICDT, volume 646 of Lecture Notes in Computer Science, page 86-98. Springer, (1992)