• Journal of Information Technology Services
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• v.8 no.4
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• pp.129-140
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• 2009

The accurate estimation of software reliability is important to a successful development in software engineering. Until recent days, the models using regression analysis based on statistical algorithm and machine learning method have been used. However, this paper estimates the software reliability using support vector regression, a sort of machine learning technique. Also, it finds the best set of optimized parameters applying immune algorithm, changing the number of generations, memory cells, and allele. The proposed IA-SVR model outperforms some recent results reported in the literature.

• Journal of the Korean Institute of Rural Architecture
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• v.13 no.2
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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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• v.16 no.1
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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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• v.37 no.3
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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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• v.36 no.3
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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.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.679-683
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• 2007

Statistical learning theory by Vapnik consists of support vector machine(SVM), support vector regression(SVR), and support vector clustering(SVC) for classification, regression, and clustering respectively. In this algorithms, SVC is good clustering algorithm using support vectors based on Gaussian kernel function. But, similar to SVM and SVR, SVC needs to determine kernel parameters and regularization constant optimally. In general, the parameters have been determined by the arts of researchers and grid search which is demanded computing time heavily. In this paper, we propose a differential evolution based SVC(DESVC) which combines differential evolution into SVC for efficient selection of kernel parameters and regularization constant. To verify improved performance of our DESVC, we make experiments using the data sets from UCI machine learning repository and simulation.

• Journal of Engineering Education Research
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• v.18 no.1
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• pp.61-73
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• 2015

The purpose of this study is to suggest some direction to support learning of students in college of engineering. It results from the assumption that engineering education accreditation should come with assessment of the educational process. To analyze the learning process, this study analyzed 5 categories - involvement in and out of instruction, faculty-student interaction, teaching-learning outcomes, and the system of student support. The Research method was questionnaire, and T-test and hierarchical linear model were used. The major findings are as follows. Major-level of satisfaction in teaching-learning and optional-level of satisfaction in teaching-learning are good. But the degree of self-directed learning activities and student-faculty interaction is low, and writing attitude and learning outcomes are not good. Student-faculty interaction, high-order thinking activities and active involvement have a good influence on learning outcomes. So this study suggests to enhance active involvement in instruction, high-order thinking activities, writing skills, and interaction with faculty for the improvement of quality of higher education.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.395-402
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• 2024

The purpose of this study is to verify the effect of the intensive support program for junior college students with low academic performance on students' learning motivation and self-learning efficacy, and to explore its meaning. This study was conducted with 46 students who participated in the JUMP-UP program supported by the D University Teaching and Learning Development Center in Busan for backers and students with low grades. The research question of this study is, first, does the JUMP-UP program affect the reinforcement of the learning motivation of junior college students? Second, does the JUMP-UP program affect the self-learning efficacy of junior college students? As a result of examining the effectiveness by conducting a survey before and after participating in the JUMP-UP program, the JUMP-UP program showed statistically significant changes in all items of participating learners' learning motivation and self-learning efficacy. Through this, it can be seen that an intensive support program such as the JUMP-UP program is valuable as a support program suitable for improving the learning motivation and self-learning efficacy of vocational college students suffering from low academic performance.

• Journal of Fisheries and Marine Sciences Education
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• v.23 no.1
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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.

• The Korean Journal of Emergency Medical Services
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• v.20 no.3
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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.