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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Electrical Engineering and Information Science Journal of Electrical Engineering and Information Science Vol.3 No.5
- 발행연도
- 1998.10
- 수록면
- 551 - 562 (12page)
이용수
초록· 키워드
오류제보하기
The learning speed of a back-propagation neural network greatly depends on its learning rate. For selecting proper learning rates, many approaches-empirical, deterministic, and stochastic methods-have been introduced to date. Some researchers have also tried to find sub-optimal learning rates using various techniques at each training step. This paper proposes a new stochastic method. Our method selects sub-optimal learning rates by an evolutionary adaptation of learning rates for each layer at every training step. We experimented with one relatively simple mapping problem and two complex mapping problems using our method and three typical adaptive methods. Through experiments, we found that the performance of our method is superior to those of the three other methods, especially where the mapping problems are complex. However, evolutionary adaptation with evolutionary programming method generally takes much longer to execute than other adaptive methods. We also experimented with the effect of the performance of our method according to evolutionary adaptation intervals, called windows. This paper describes our selecting algorithm, the other three methods, the window technique, and experimental results.
목차
Abstract
Ⅰ. Introduction
Ⅱ. Basic Learning Method and Previous Learning Rate Selection Algorithms
Ⅲ. Our Learning Rate Selection Method
Ⅳ. Experimental Results and Discussion
Ⅴ. Window technique
Ⅵ. Conclusion
References
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