AClass: A simple, online, parallelizable algorithm for probabilistic classification

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Vikash K. Mansinghka, Daniel M. Roy, Ryan Rifkin, Josh Tenenbaum ;
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:315-322, 2007.

Abstract

We present AClass, a simple, online, parallelizable algorithm for supervised multiclass classification. AClass models each classconditional 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.

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