List of Tables

1. Default algorithmic parameters for SNoW's algorithms. Perceptron uses its ``learning rate'' for promotion, and the opposite thereof for demotion. Winnow uses $\alpha$ for promotion and $\beta$ for demotion.
2. Partial truth table and SNoW examples for the concept $x_2 \vee x_5$. SNoW (with default parameters) will create two target nodes, and each can be said to learn a separate concept. One learns to build a bigger activation for positive examples (label 1), and the other learns to build a bigger activation for negative examples (label 0).