论文信息 - Supervised Learning and Divide-and-Conquer: A Statistical Approach

Supervised Learning and Divide-and-Conquer: A Statistical Approach

We present a novel statistical model for supervised learning. The model is based on the principle of divide-and-conquer, and is similar in spirit to models such as CART, ID3 and MARS. We formulate the problem of learning the parameters of the model as a maximum likelihood estimation problem and develop an Expectation-Maximization (EM) algorithm for the model. Comparative simulation results are presented in the robot dynamics domain.

Michael I. Jordan | Robert A. Jacobs | R. Jacobs

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