• 한국농촌건축학회논문집
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• 제13권2호
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• pp.47-54
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• 2011

According to the master plan of the Ministry of Education, Science and Technology, departmentalized class system will be extended to all general middle & high schools by 2014 with the exception only of those having less than 6 classes located in small cities in rural areas. Under departmentalized class system, according to class timetable, students need to move from classroom to another classroom and areas where homebases, lounges, media spaces, rest places, and etc. This study has been undertaken to provide architectural data required in planning for student & learning support space for schools operating departmentalized class system, by investigating and analyzing cases in use at schools operating the system in Chungbuk area. As departmentalized class system is increasingly introduced, student & learning support space should be understood newly as spaces indispensable for students.

• Management Science and Financial Engineering
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• 제16권1호
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• pp.81-117
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• 2010

Semi-supervised learning incorporates unlabeled examples, whose labels are unknown, as well as labeled examples into learning process. Although transductive support vector machine (TSVM), one of semi-supervised learning models, was proposed about a decade ago, its application to large-scaled data has still been limited due to its high computational complexity. Our previous research addressed this limitation by introducing a branch-and-bound algorithm for finding an optimal solution to TSVM. In this paper, we propose three new techniques to enhance the performance of the branch-and-bound algorithm. The first one tightens min-cut bound, one of two bounding strategies. Another technique exploits a graph-based approximation to a support vector machine problem to avoid the most time-consuming step. The last one tries to fix the labels of unlabeled examples whose labels can be obviously predicted based on labeled examples. Experimental results are presented which demonstrate that the proposed techniques can reduce drastically the number of subproblems and eventually computational time.

• East Asian mathematical journal
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• 제37권3호
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• pp.333-354
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• 2021

A support vector machine (SVM) is a state-of-the-art machine learning model rooted in structural risk minimization. SVM is underestimated with regards to its application to real world problems because of the difficulties associated with its use. We aim at showing that the performance of SVM highly depends on which kernel function to use. To achieve these, after providing a summary of support vector machines and kernel function, we constructed experiments with various benchmark datasets to compare the performance of various kernel functions. For evaluating the performance of SVM, the F1-score and its Standard Deviation with 10-cross validation was used. Furthermore, we used taylor diagrams to reveal the difference between kernels. Finally, we provided Python codes for all our experiments to enable re-implementation of the experiments.

• Journal of the Korean Statistical Society
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• 제36권3호
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• pp.367-372
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• 2007

Classification as a tool to extract information from data plays an important role in science and engineering. Among various classification methodologies, support vector machine has recently seen significant developments. The central problem this paper addresses is the accuracy of support vector machine. In particular, we are interested in the situations where fast rates of convergence to the Bayes risk can be achieved by support vector machine. Through learning examples, we illustrate that support vector machine may yield fast rates if the space spanned by an adopted kernel is sufficiently large.

• 한국지능시스템학회논문지
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• 제17권5호
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• pp.679-683
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• 2007

Vapnik의 통계적 학습이론은 분류, 회귀, 그리고 군집화를 위하여 SVM(support vector machine), SVR(support vector regression), 그리고 SVC(support vector clustering)의 3가지 학습 알고리즘을 포함한다. 이들 중에서 SVC는 가우시안 커널함수에 기반한 지지벡터를 이용하여 비교적 우수한 군집화 결과를 제공하고 있다. 하지만 SVM, SVR과 마찬가지로 SVC도 커널모수와 정규화상수에 대한 최적결정이 요구된다 하지만 대부분의 분석작업에서 사용자의 주관적 경험에 의존하거나 격자탐색과 같이 많은 컴퓨팅 시간을 요구하는 전략에 의존하고 있다. 본 논문에서는 SVC에서 사용되는 커널모수와 정규화상수의 효율적인 결정을 위하여 차분진화를 이용한 DESVC(differential evolution based SVC)를 제안한다 UCI Machine Learning repository의 학습데이터와 시뮬레이션 데이터 집합들을 이용한 실험을 통하여 기존의 기계학습 알고리즘과의 성능평가를 수행한다.

• 공학교육연구
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• 제18권1호
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• pp.61-73
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• 2015

이 연구의 목적은 공학교육의 질 개선을 위해서는 학생들의 학습과정에 대한 분석이 함께 이루어져야 한다는 가정 아래, 공과대학생들의 학습과정을 분석하고 이를 통해 학습지원의 방향을 탐색하는 것이다. 학습과정 분석은 다섯 가지 영역-수업내활동, 수업외활동, 상호작용, 학습성과, 학습지원체제-으로 이루어졌으며, 수도권에 위치한 S대학의 공과대학생들을 대상으로 설문 조사를 하였다. T-test, Anova, 위계적선형모형을 활용한 회귀분석을 활용하였다. 연구 결과 전공과 교양 수업 만족도는 높았으나, 자기주도적 학습활동이나 교수와의 상호작용은 낮았으며, 고등사고력 활동에 많이 참여하고 있지 않았고 학습성과 역시 낮았다. 학습성과에 중요한 영향을 미치는 요인은 교수와의 상호작용, 고등사고력 활동, 수업에의 적극적인 참여였다. 이러한 결과를 토대로 고등교육의 질 개선을 위해 수업에의 적극적 참여와 고등사고력 활동을 강조하고 글쓰기 지원 및 교수-학생간 상호작용 활성화 등을 제안하였다.

• 문화기술의 융합
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• 제10권1호
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• pp.395-402
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• 2024

본 연구의 목적은 학업부진 전문대학생을 위한 집중 지원 프로그램이 학생들의 학습동기 및 자기학습효능감에 미치는 효과를 검증하고 의미를 탐색하는 것이다. 본 연구는 부산광역시 D대학교 교수학습개발센터가 학사경고자 및 성적부진 학생들을 대상으로 지원한 JUMP-UP 프로그램에 참여한 46명의 학생을 대상으로 진행되었다. 본 연구의 연구문제는 첫째, JUMP-UP 프로그램은 전문대학생의 학습동기 강화에 영향을 미치는가? 둘째, JUMP-UP 프로그램은 전문대학생의 자기학습효능감에 영향을 미치는가?이다. JUMP-UP 프로그램 참여 전·후 설문조사를 실시하여 효과성을 살펴본 결과 JUMP-UP 프로그램은 참여 학습자의 학습동기와 자기학습효능감 모든 항목에서 통계적으로 유의미한 변화를 나타내었다. 이를 통하여 JUMP-UP 프로그램과 같은 집중 지원 프로그램은 학업부진을 겪는 전문대학생의 학습동기 및 자기학습효능감 향상에 적합한 지원 프로그램으로 가치가 있다는 것을 알 수 있다.

• 수산해양교육연구
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• 제23권1호
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• pp.116-125
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• 2011

In this study, a survey research was conducted on government employees in Busan Metropolitan City to identify the influence of cyber education factors (learning factor, learner factor, and learning system factor) on the preference for government employee cyber education offered by the government always learning system. Analyzed results, recognition of learning factor, learner factor, and always learning system were shown to have significant influence on the preference for cyber education, but no indication of influence by always learning support. This study intends to assist stimulating voluntary participation in cyber education and active commitment in learning activities through improving learning effect and fortifying convenient informatization education, with regard to activation of cyber education and improved preference for cyber education.

• 한국응급구조학회지
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• 제20권3호
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• pp.21-35
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• 2016

Purpose: The purpose of the study was to investigate the continuous effect of advanced cardiovascular life support (ACLS) simulation education according to Felder-Silverman learning style. Methods: A self-reported questionnaire was completed by 94 students of emergency medical technology and nursing. There were 50 female students (53.2%) and 88 students (93.6%) had basic life support certification. The study instruments included knowledge, performance, and confidence. Data were analyzed using SPSS v. 20.0. Results: The learning style consisted of reflective type (51.1%), sensory type (76.6%), visual type (63.8%), and sequential type (64.9%). There was a significant difference in continuous effect on performance by learning type. Conclusion: It is necessary to identify the learning style of students before simulation education in order to maintain continuous effect of ACLS education.

• 한국인공지능학회지
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• 제7권2호
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• pp.19-24
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• 2019

Lung cancer is a chronic disease which ranks fourth in cancer incidence with 11 percent of the total cancer incidence in Korea. To deal with such issues, there is an active study on the usefulness and utilization of the Clinical Decision Support System (CDSS) which utilizes machine learning. Thus, this study reviews existing studies on artificial intelligence technology that can be used in determining the lung cancer, and conducted a study on the applicability of machine learning in determination of the lung cancer by comparison and analysis using Azure ML provided by Microsoft. The results of this study show different predictions yielded by three algorithms: Support Vector Machine (SVM), Two-Class Support Decision Jungle and Multiclass Decision Jungle. This study has its limitations in the size of the Big data used in Machine Learning. Although the data provided by Kaggle is the most suitable one for this study, it is assumed that there is a limit in learning the data fully due to the lack of absolute figures. Therefore, it is claimed that if the agency's cooperation in the subsequent research is used to compare and analyze various kinds of algorithms other than those used in this study, a more accurate screening machine for lung cancer could be created.