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Efficient Regularized Piecewise-Linear Regression Trees
We present a detailed analysis of the class of regression decision tree algorithms which employ a regulized piecewise-linear node-splitting criterion and have regularized linear models at the leaves.
Reservoir Boosting : Between Online and Offline Ensemble Learning
We propose to train an ensemble with the help of a reservoir in which the learning algorithm can store a limited number of samples.
Joint Cascade Optimization Using A Product Of Boosted Classifiers
The standard strategy for efficient object detection consists of building a cascade composed of several binary classifiers.
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