지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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초록· 키워드
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The purpose of this study is to claim the validity of tuning the architecture of neural network models for multi-class classification. A neural network model for multi-class classification is basically constructed by building a series of neural network models for binary classification. Building a neural network model, we are required to set the values of parameters such as number of hidden nodes and weight decay parameter in advance, which draws special attention as the performance of the model can be cute different by the values of the parameters. For better performance of the model, it is absolutely necessary to have a prior process of tuning the parameters every time the neural network model is built. Nonetheless, previous studies have not mentioned the necessity of the tuning process or proved its validity. In this study, we claim that we should tune the parameters every time we build the neural network model for multi-class classification. Through empirical analysis using wine data, we show that the performance of the model with the tuned parameters is superior to those of untuned models.
목차
Abstract
1. 서론
2. 이론적 배경
3. 실험 디자인
4. 실증 분석
5. 결론
참고문헌
참고문헌 (20)
참고문헌 신청
1. [학술지(정기간행물)] - 김다윗 / 2007 / 신경망 분리모형과 사례기반추론을 이용한기업 신용 평가 / Journal of Information Technology Applications & Management 14 (2) : 151 ~ 168
2. [학술지(정기간행물)] - Altman, E.I / 1994 / Corporate distress diagnosis comparisons using discriminant analysis and neural networks / Journal of Banking and Finance 18 (3) : 505 ~ 529
3. [단행본] - Bartlett, P.L / 1997 / For valid generalization, the size of the weights is more important than the size of the network, In Advances in Neural Information Processing Systems, Vol.9 / The MIT Press
4. [단행본] - Berry, M.J.A / 2004 / Data Mining Techniques For Marketing, Sales, and Customer Relationship Management / Wiley
5. [단행본] - Bishop, C.M / 1995 / Neural networks for pattern recognition / Oxford University Press
2. [학술지(정기간행물)] - Altman, E.I / 1994 / Corporate distress diagnosis comparisons using discriminant analysis and neural networks / Journal of Banking and Finance 18 (3) : 505 ~ 529
3. [단행본] - Bartlett, P.L / 1997 / For valid generalization, the size of the weights is more important than the size of the network, In Advances in Neural Information Processing Systems, Vol.9 / The MIT Press
4. [단행본] - Berry, M.J.A / 2004 / Data Mining Techniques For Marketing, Sales, and Customer Relationship Management / Wiley
5. [단행본] - Bishop, C.M / 1995 / Neural networks for pattern recognition / Oxford University Press
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UCI(KEPA) : I410-ECN-0101-2014-320-003164834