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Tree Based Ensemble Models Regularization by Convex Optimization
This video was recorded at NIPS Workshops, Whistler 2009. Tree based ensemble methods can be seen as a way to learn a kernel from a sample of input-output pairs. This paper proposes a regularization framework to incorporate non-standard information not used in the kernel learning algorithm, so as to take advantage of incomplete information about output values and/or of some prior information about the problem at hand. To this end a generic convex optimization problem is formulated which is first customized into a manifold regularization approach for semi-supervised learning, then as a way to exploit censored output values, and finally as a generic way to exploit prior information about the problem.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
