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» Relative Margin Machines
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118
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COLT
1999
Springer
15 years 8 months ago
Boosting as Entropy Projection
We consider the AdaBoost procedure for boosting weak learners. In AdaBoost, a key step is choosing a new distribution on the training examples based on the old distribution and th...
Jyrki Kivinen, Manfred K. Warmuth
EUROCOLT
1999
Springer
15 years 8 months ago
Averaging Expert Predictions
We consider algorithms for combining advice from a set of experts. In each trial, the algorithm receives the predictions of the experts and produces its own prediction. A loss func...
Jyrki Kivinen, Manfred K. Warmuth
138
Voted
EUROCOLT
1999
Springer
15 years 8 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...
122
Voted
IEEEPACT
1998
IEEE
15 years 8 months ago
Athapascan-1: On-Line Building Data Flow Graph in a Parallel Language
In order to achieve practical efficient execution on a parallel architecture, a knowledge of the data dependencies related to the application appears as the key point for building...
François Galilée, Jean-Louis Roch, G...
108
Voted
ALT
1998
Springer
15 years 8 months ago
Predictive Learning Models for Concept Drift
Concept drift means that the concept about which data is obtained may shift from time to time, each time after some minimum permanence. Except for this minimum permanence, the con...
John Case, Sanjay Jain, Susanne Kaufmann, Arun Sha...