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인공신경망 기초 의사결정트리 분류기에 의한 시계열모형화에 관한 연구
오상봉 한국시뮬레이션학회 1996 한국시뮬레이션학회 논문지 Vol.5 No.1
We propose a new approach to classifying a time series data into one of the autoregressive moving-average (ARMA) models. It is bases on two pattern recognition concepts for solving time series identification. The one is an extended sample autocorrelation function (ESACF). The other is a neural network-driven decision tree classifier(NNDTC) in which two pattern recognition techniques are tightly coupled : neural network and decision tree classfier. NNDTc consists of a set of nodes at which neural network-driven decision making is made whether the connecting subtrees should be pruned or not. Therefore, time series identification problem can be stated as solving a set of local decisions at nodes. The decision values of the nodes are provided by neural network functions attached to the corresponding nodes. Experimental results with a set of test data and real time series data show that the proposed approach can efficiently identify the time seires patterns with high precision compared to the previous approaches.
WWW상에서의 Personalized Advertising에 관한 연구
오상봉 大田大學校 産業技術硏究所 1998 산업기술연구소 論文集 Vol.9 No.1
The growth of the Internet offers a vision of one to one advertising tailored to consumers characteristics. We propose an architecture for Personalized Advertising (PA) in shopping mall built on the World Wide Web, which is composed of three steps: 1) Consumer-oriented Advertising which uses consumer characteristics and product characteristics into consideration; 2) Conflict resolution between Consumer-oriented Advertising and pricing scheme made by advertiser and advertising company; 3) Lay-out of the Web pages which are delivered to visitors. We also suggest willingness to visit (WTV) measure, which can be used to estimate a consumers willingness to see the web page of a specific product. The procedure for Consumer-oriented Advertising is suggested and directions for future research are provided.