AClass: A Simple, Online, Parallelizable Algorithm for Probabilistic Classification
Abstract
We present AClass, a simple, online, parallelizable algorithm for supervised multiclass classification. AClass models each class-conditional density as a Chinese restaurant process mixture, and performs approximate inference in this model using a sequential Monte Carlo scheme. AClass combines several strengths of previous approaches to classification that are not typically found in a single algorithm; it supports learning from missing data and yields sensibly regularized nonlinear decision boundaries while remaining computationally efficient. We compare AClass to several standard classification algorithms and show competitive performance.
Cite
Text
Mansinghka et al. "AClass: A Simple, Online, Parallelizable Algorithm for Probabilistic Classification." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.Markdown
[Mansinghka et al. "AClass: A Simple, Online, Parallelizable Algorithm for Probabilistic Classification." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.](https://mlanthology.org/aistats/2007/mansinghka2007aistats-aclass/)BibTeX
@inproceedings{mansinghka2007aistats-aclass,
title = {{AClass: A Simple, Online, Parallelizable Algorithm for Probabilistic Classification}},
author = {Mansinghka, Vikash K. and Roy, Daniel M. and Rifkin, Ryan and Tenenbaum, Josh},
booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics},
year = {2007},
pages = {315-322},
volume = {2},
url = {https://mlanthology.org/aistats/2007/mansinghka2007aistats-aclass/}
}