The active learning strategy (6) outperforms passive learning on small Ising perceptrons: (a) entropy density s (in units of bit), (b) generalization error ε. Training patterns are added one after another, α = P/N is the instantaneous density of patterns (N is the number of input neurons and P is the number of training patterns). Each data point is obtained by averaging over 1,000 independent runs of the passive (Random) or the active (Design) learning algorithm.