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ICML
1994
IEEE
13 years 11 months ago
Efficient Algorithms for Minimizing Cross Validation Error
Model selection is important in many areas of supervised learning. Given a dataset and a set of models for predicting with that dataset, we must choose the model which is expected...
Andrew W. Moore, Mary S. Lee
COLT
2006
Springer
13 years 11 months ago
A Randomized Online Learning Algorithm for Better Variance Control
We propose a sequential randomized algorithm, which at each step concentrates on functions having both low risk and low variance with respect to the previous step prediction functi...
Jean-Yves Audibert
TKDE
2008
123views more  TKDE 2008»
13 years 7 months ago
Explaining Classifications For Individual Instances
We present a method for explaining predictions for individual instances. The presented approach is general and can be used with all classification models that output probabilities...
Marko Robnik-Sikonja, Igor Kononenko
COLT
2010
Springer
13 years 5 months ago
Regret Minimization With Concept Drift
In standard online learning, the goal of the learner is to maintain an average loss that is "not too big" compared to the loss of the best-performing function in a fixed...
Koby Crammer, Yishay Mansour, Eyal Even-Dar, Jenni...
IGARSS
2010
13 years 2 months ago
Calibrating probabilities for hyperspectral classification of rock types
This paper investigates the performance of machine learning methods for classifying rock types from hyperspectral data. The main objective is to test the impact on classification ...
Sildomar T. Monteiro, Richard J. Murphy