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- W2128266854 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. For well-behaved input distributions, if this algorithm converges to the optimal solution, its learning curve can decrease as fast as P-1." @default.
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- W2128266854 date "1994-01-01" @default.
- W2128266854 modified "2023-09-24" @default.
- W2128266854 title "On-line Learning of Dichotomies" @default.
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