On-line Learning of Dichotomies

The performance of on-line algorithms for learning dichotomies is studied. In on-line learning, the number of examples P is equivalent to the learning time, since each example is presented only once. The learning curve, or generalization error as a function of P, depends on the schedule at which the learning rate is lowered. For a target that is a perceptron rule, the learning curve of the perceptron algorithm can decrease as fast as P;1, if the schedule is optimized. If the target is not realizable by a perceptron, the perceptron algorithm does not generally converge to the solution with lowest generalization error. For the case of unrealizability due to a simple output noise, we propose a new on-line algorithm for a perceptron yielding a learning curve that can approach the optimal generalization error as fast as P;1=2. We then generalize the perceptron algorithm to any class of thresholded smooth functions learning a target from that class. For \well-behaved" input distributio...

• Type: Conference
• Year: 1994
• Where: NIPS
• Authors: N. Barkai, H. Sebastian Seung, Haim Sompolinsky
• Keywords: Algorithm, Learning Curve, NIPS 1994, NIPS 2007, Perceptron Algorithm