Abstract
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.
Original language | English |
---|---|
Pages | 303-310 |
Number of pages | 8 |
State | Published - 1994 |
Event | 7th International Conference on Neural Information Processing Systems, NIPS 1994 - Denver, United States Duration: 1 Jan 1994 → 1 Jan 1994 |
Conference
Conference | 7th International Conference on Neural Information Processing Systems, NIPS 1994 |
---|---|
Country/Territory | United States |
City | Denver |
Period | 1/01/94 → 1/01/94 |
Bibliographical note
Publisher Copyright:© NIPS 1994.All rights reserved.