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 peforms 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.

@inproceedings{aclassManRoyRifTenAISTATS07,
 title		= "AClass: An online algorithm for generative classification",
 author		= "Vikash K. Mansinghka and Daniel M. Roy and Ryan Rifkin and Joshua B. Tenenbaum",
 booktitle	= "Proceedings of the Eleventh International Workshop on Artificial Intelligence and Statistics (AI-STATS 07), Puerto Rico.",
 year		= "2007",
}