Supervised Learning and Divide-and-Conquer: A Statistical Approach
Abstract
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.
Cite
Text
Jordan and Jacobs. "Supervised Learning and Divide-and-Conquer: A Statistical Approach." International Conference on Machine Learning, 1993. doi:10.1016/B978-1-55860-307-3.50027-7Markdown
[Jordan and Jacobs. "Supervised Learning and Divide-and-Conquer: A Statistical Approach." International Conference on Machine Learning, 1993.](https://mlanthology.org/icml/1993/jordan1993icml-supervised/) doi:10.1016/B978-1-55860-307-3.50027-7BibTeX
@inproceedings{jordan1993icml-supervised,
title = {{Supervised Learning and Divide-and-Conquer: A Statistical Approach}},
author = {Jordan, Michael I. and Jacobs, Robert A.},
booktitle = {International Conference on Machine Learning},
year = {1993},
pages = {159-166},
doi = {10.1016/B978-1-55860-307-3.50027-7},
url = {https://mlanthology.org/icml/1993/jordan1993icml-supervised/}
}