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Development of Intelligent Credit Rating System using Support Vector Machines
김경재,Kim Kyoung-jae The Korea Institute of Information and Commucation 2005 한국정보통신학회논문지 Vol.9 No.7
In this paper, I propose an intelligent credit rating system using a bankruptcy prediction model based on support vector machines (SVMs). SVMs are promising methods because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study examines the feasibility of applying SVM in Predicting corporate bankruptcies by comparing it with other data mining techniques. In addition. this study presents architecture and prototype of intelligeht credit rating systems based on SVM models.
김경재,이두선,Kim, Kyung-Jae,Lee, Doo-Sun 대한소아외과학회 1996 소아외과 Vol.2 No.2
The management of twenty-two children with blunt abdominal trauma was analyzed. Nineteen cases had intraabdominal injuries; involving the spleen in 7 cases, the liver in 5, the pancreas in one and the bladder in one. There were five case multiple intraabdominal organ injuries. Seventeen out of 19 patients were treated non-operatively, but one was operated upon later because of delayed bleeding. Thirteen patients required transfusion in the non operated group, the mean values of the Pediatric Trauma Score (PTS) was 11.3. The mean lowest hemoglobulin(LHb) was 9.1 g/dL. The mean value of three cases with extraabdominal injuries were 9.0 and 8.3 g/dL respectively. The average amount of transfusion was 17.3 ml/kg. In the operated group, 2 cases were transfused an average of 139.8 ml/kg and their mean PTS was 5 and LHb was 6.6 g/dL. In one out of 16 non-operated cases, intrahepatic hematoma developed and but resolved conservatively. However, two out of 3 operated cases suffer complications such as an intubation granuloma and an intraabdominal abscess with wound dehescence. In conclusion, non-operative management in child with blunt abdominal trauma was safe in Grade I and II solid organ injuries. The decision for operation should be based on the hemodynamic stability after initial resuscitation including transfusion.
Recommender System using Implicit Trust-enhanced Collaborative Filtering
Kyoung-jae Kim(김경재),Youngtae Kim(김영태) 한국지능정보시스템학회 2013 지능정보연구 Vol.19 No.4
개인화는 개인적인 기호를 바탕으로 각 사용자에게 맞춤화된 컨텐츠를 제공하는 것을 목표로 한다. 이러한 관점에서, 개인화의 핵심적인 부분은 각 사용자의 기호에 적합한 컨텐츠나 상품을 추천할 수 있는 추천기술이라 할 수 있다. 선행연구들은 추천시스템의 중요성을 인지하고 새로운 추천기술을 제안하여 왔다. 여러 추천기술들 중에서 협업필터링은 실무에서 활발하게 연구되고 활용되어 왔다. 그러나, 협업필터링은 종종 희박성 또는 확장성 문제를 겪게 된다. 선행연구들 역시 이 두 가지 문제점의 중요성을 인지하고 그에 대한 여러 가지 해결방안들을 제안하였다. 하지만, 여러 선행연구들은 기존의 사용자-상품 매트릭스 외에 다른 원천들로부터 생성된 추가적인 정보를 이용함으로써 문제점들을 해결하려 함으로 인하여 추가적인 시간과 비용을 요하는 다른 문제를 야기하였다. 본 연구에서는 희박성 문제를 완화하고 추천시스템의 성능을 개선하기 위하여 협업필터링을 위한 새로운 내재적 평가방법을 제안한다. 즉, 본 연구에서는 기존 사용자-상품 매트릭스를 이용하여 사용자 간의 신뢰수준을 측정할 수 있는 내재적 평가법에 기반한 사용자-상품 매트릭스의 보완을 통해 희박성 문제를 완화할 수 있는 방안을 제안한다. 또한, 본 연구에서는 제안하는 방안의 유용성을 평가하기 위한 탐색적 실험 결과를 제공한다. Personalization aims to provide customized contents to each user by using the user’s personal preferences. In this sense, the core parts of personalization are regarded as recommendation technologies, which can recommend the proper contents or products to each user according to his/her preference. Prior studies have proposed novel recommendation technologies because they recognized the importance of recommender systems. Among several recommendation technologies, collaborative filtering (CF) has been actively studied and applied in real-world applications. The CF, however, often suffers sparsity or scalability problems. Prior research also recognized the importance of these two problems and therefore proposed many solutions. Many prior studies, however, suffered from problems, such as requiring additional time and cost for solving the limitations by utilizing additional information from other sources besides the existing user-item matrix. This study proposes a novel implicit rating approach for collaborative filtering in order to mitigate the sparsity problem as well as to enhance the performance of recommender systems. In this study, we propose the methods of reducing the sparsity problem through supplementing the user-item matrix based on the implicit rating approach, which measures the trust level among users via the existing user-item matrix. This study provides the preliminary experimental results for testing the usefulness of the proposed model.