The timed aggregate perceptron (TAP) classifier (Medlock, forthcoming) is a highly scalable linear classifier which has been shown to outperform SVMs and Bayesian logistic regression (BLR) on topic and other text classification tasks. The TAP classifier achieved better classification accuracy than either popular alternative, but trained in near linear time.

This means that a classifier trained on the entire Reuters Rcv1 corpus of around 800K news stories (Lewis etal, 2004) divided into 103 classes could be built in around 3.5hrs CPU time (as opposed to around 20hrs for the SVM or 50hrs for BLR). This is a significant advantage for real world applications where reductions in training time allow vital experimentation into enhancing feature generation and selection as well as frequent retraining as data is accumulated.