On-line Learning of Dichotomies

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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...
N. Barkai, H. Sebastian Seung, Haim Sompolinsky
Added 02 Nov 2010
Updated 02 Nov 2010
Type Conference
Year 1994
Where NIPS
Authors N. Barkai, H. Sebastian Seung, Haim Sompolinsky
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