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Online Gradient Boosting

NeurIPS 2015 • Alina Beygelzimer • Elad Hazan • Satyen Kale • Haipeng Luo

We extend the theory of boosting for regression problems to the online learning setting. Generalizing from the batch setting for boosting, the notion of a weak learning algorithm is modeled as an online learning algorithm with linear loss functions that competes with a base class of regression functions, while a strong learning algorithm is an online learning algorithm with convex loss functions that competes with a larger class of regression functions... (read more)

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