http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : 서용무 ( Yong Moo Suh ) (검색결과 6 건)
- 무료
- 기관 내 무료
- 유료
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곽기효,서용무,Kwak, Ki-Hyo,Suh, Yong-Moo 한국국방경영분석학회 2007 한국국방경영분석학회지 Vol.33 No.2
The period of military personnel service will be phased down by 2014 according to 'The law of National Defense Reformation' issued by the Ministry of National Defense. For this reason, the ROK army provides discrimination education to 'newly recruited privates' for more effective individual performance in the on-the-job training. For the training to be more effective, it would be essential to predict the degree of achievements by new privates in the training. Thus, we used data mining techniques to develop a classification model which classifies the new privates into one of two achievements groups, so that different skills of education are applied to each group. The target variable for this model is a binary variable, whose value can be either 'a group of general control' or 'a group of special control'. We developed four pure classification models using Neural Network, Decision Tree, Support Vector Machine and Naive Bayesian. We also built four hybrid models, each of which combines k-means clustering algorithm with one of these four mining technique. Experimental results demonstrated that the highest performance model was the hybrid model of k-means and Neural Network. We expect that various military education programs could be supported by these classification models for better educational performance.
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크라우드소싱 커뮤니티 내 고객 선호와 조직의 혁신수용 비교 연구 : MyStarbucksIdea.com의 고객 아이디어 분석을 중심으로
이한준,서용무,Lee, Han-Jun,Suh, Yong-Moo 한국IT서비스학회 2013 한국IT서비스학회지 Vol.12 No.1
Open innovation concept is advocating the importance of the customer roles in firm's innovation. As a result, crowdsourcing community is drawing attention as a strategic asset for open innovation across diverse industries. Considering that the goal of crowdsourcing community is harnessing innovative ideas, understanding the characteristics of user-favorable and organization-adoptable ideas can enhance the effectiveness of idea crowdsourcing. In our approach, we extract idea content-based characteristics such as subjectivity, negativity, prosocialneess, and depth of idea to examine what are the factors that affect user preference and organizational adoption. An analysis of 71,134 ideas from MyStarbucksIdea.com shows that there are significant differences between user-favorable and organization-adoptable ideas in terms of idea characteristics. Lastly, both theoretical and managerial implications are discussed.
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김기운(Gi Un Kim),서용무(Yong Moo Suh) 한국경영학회 2001 경영학연구 Vol.30 No.3
Work done so far in the area of data warehousing has concentrated mainly both on building a data warehouse and on maintaining it. But, little attention has been paid to the area of using and analyzing warehouse data. While quite a few commercial OLAP tools for using warehouse data have become available, companies still have difficulties in selecting an OLAP tool which fits their specific purposes. In this paper, we propose a list of detailed and concrete items to be evaluated before deciding to select an OLAP tool, and describe two cases of verifying the usefulness of those items as a basis to select a tool. The items were derived on the basis of the desired OLAP features suggested by Codd, Pende and Raden. In the first verification case, we evaluated three ROLAP tools such as Brio Enterprise. InfoBeacon, and DSS Agent and in the second, we evaluated the ROLAP tool, DSS Agent, selected as the best in the first verification, and a MOLAP tool, EssBASE, holding the first market share in the worldwide OLAP market. What we have learned from the first verification is that the three ROLAP tools have some weakness in advanced statistical analyses, API for interface to client tool, built-in user-defined functions, aggregation functions, metadata management, and Korean character support. And the lessons from the second are that though the ROLAP tool has more improved features than the other tools in the first verification, it is still not satisfactory in supporting aggregation functions and Korean characters, and that the MOLAP tool also has the problem in supporting aggregation functions and Korean characters and in supporting metadata such as the compatibility with ETT metadata. From the two verifications, we concluded that the evaluation items are detailed and concrete enough to be used as a basis for selecting an OLAP tool. Further more, we learned that ROLAP tools are more appropriate for enterprise data warehouse and MOLAP for a specific data mart.
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신경망과 의사결정 나무를 이용한 충수돌기염 환자의 재원일수 예측모형 개발
정석훈(Chung, Suk-Hoon),한우석(Han, Woo-Sok),서용무(Suh, Yong-Moo),이현실(Rhee, Hyun-SiIl) 한국산학기술학회 2009 한국산학기술학회논문지 Vol.10 No.6
충수돌기염 환자의 LoS(Length of Stay)를 예측하는 것은 병상의 운영에 적지 않은 영향을 준다. 본 논문에서는 Neural Networks와 Decision Tree를 이용하여 LoS와 연관이 높은 입력변수들을 찾아 그 의미를 분석하며, 찾아낸 입력변수들을 이용하여 다양한 LoS 예측 모형을 개발하고 그 성능을 비교하였다. 모형의 예측 정확성을 높이기 위하여 Bagging과 Boosting 등의 Ensemble 기법도 적용하였다. 실험 결과, Decision Tree 모형이 Neural Networks 모형보다 좀 더 적은 수의 속성을 가지고도 거의 통일한 예측력을 보였으며, Ensemble 기법 중에서는 Bagging 기법이 Boosting 기법보다 좋은 결과를 보여주었다. 의사결정나무 기법은 Neural Networks 기법에 비해 설명력이 있으며, 충수돌기염의 LoS 예측에 매우 효과적이었고, 중요 입력 변수의 선정에도 좋은 결과를 보여줌에 따라 향후 적극적인 기법의 도입이 필요하다고 할 수 있다. For the efficient management of hospital sickbeds, it is important to predict the length of stay (LoS) of appendicitis patients. This study analyzed the patient data to find factors that show high positive correlation with LoS, build LoS prediction models using neural network and decision tree models, and compare their performance. In order to increase the prediction accuracy, we applied the ensemble techniques such as bagging and boosting. Experimental results show that decision tree model which was built with less number of variables shows prediction accuracy almost equal to that of neural network model, and that bagging is better than boosting. In conclusion, since the decision tree model which provides better explanation than neural network model can well predict the LoS of appendicitis patients and can also be used to select the input variables, it is recommended that hospitals make use of the decision tree techniques more actively.
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