# On-line learning of non-monotonic rules by simple perceptron

@article{Inoue1997OnlineLO, title={On-line learning of non-monotonic rules by simple perceptron}, author={Jun-ichi Inoue and Hidetoshi Nishimori and Yoshiyuki Kabashima}, journal={arXiv: Condensed Matter}, year={1997} }

We study the generalization ability of a simple perceptron which learns unlearnable rules. The rules are presented by a teacher perceptron with a non-monotonic transfer function. The student is trained in the on-line mode. The asymptotic behaviour of the generalization error is estimated under various conditions. Several learning strategies are proposed and improved to obtain the theoretical lower bound of the generalization error.

## 5 Citations

Statistical Mechanics of On-line Learning when a Moving Teacher Goes around an Unlearnable True Teacher

- Computer Science, PhysicsArXiv
- 2006

It is shown that the generalization errors of a student can temporarily become smaller than that of a moving teacher and can reach the lowest value, even if the student only uses examples from the moving teacher.

On-line Learning of an Unlearnable True Teacher through Mobile Ensemble Teachers

- Computer Science, PhysicsArXiv
- 2008

The generalization performance of the student is shown to exceed that of the ensemble teachers in a transient state, as was shown in similar ensemble-teachers models.

Statistical mechanics of ensemble learning using stochastic filtering

- 2006

We analyze ensemble learning using Murata’s stochastic filtering with statistical mechanical method based on online learning theory. It is shown that the filtering is effective to keep students’…

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