• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.13-20
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• 2011

The face recognition has been used in a variety fields, such as identification and security. The procedure of the face recognition is as follows; extracting face features of face images, learning the extracted face features, and selecting some features among all extracted face features. The selected features have discrimination and are used for face recognition. However, there are numerous face features extracted from face images. If a face recognition system uses all extracted features, a high computing time is required for learning face features and the efficiency of computing resources decreases. To solve this problem, many researchers have proposed various Boosting methods, which improve the performance of learning algorithms. Mutual-Boost is the typical Boosting method and efficiently selects face features by using mutual information between two features. In this paper, we propose a GroupMutual-Boost method for improving Mutual-Boost. Our proposed method can shorten the time required for learning and recognizing face features and use computing resources more effectively since the method does not learn individual features but a feature group.

• Knowledge Management Research
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• v.13 no.3
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• pp.37-54
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• 2012

The purpose of this study is to investigate determinants of partner opportunism in Korean discount store distribution channels. In addition, this study also try to examine moderating role of relational learning in the relationship. This study deals with transaction specific investment asymmetry, mutual hostages, payoff inequity, cultural diversity, and goal incompatibilities as determinants of partner opportunism. For empirical testing, 293 respondents of suppliers of discount store in Korea were surveyed and the analysis utilizing partial least square model indicated that TSI asymmetry, payoff inequity, and goal incompatibilities had positive effects on partner opportunism. On the other hand, mutual hostages had negative effect on partner opportunism. In addition, relational learning had moderating effect on the relationship between TSI asymmetry, mutual hostages, and payoff inequity and partner opportunism.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.5 no.6
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• pp.317-325
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• 2015

The aim of this research was to investigate the relationship among team learning behavior, individual creativity, team shared mental model(TSMM), mutual performance monitoring on team creativity and then providing the fundamental data on the education. Also it intended to acknowledge relative predictive power on team creativity of independent variables. The total of 257 college students participated the team learning for 6 weeks in a semester. Pearson's product moment correlation and regression analysis were used for data analysis and testing of significance of verification, The main research results are summarized as follows; team learning behavior, TSMM, mutual performance monitoring had no significant effects on three subfactors of team creativity such as novelty, resolution, elaboration & synthesis. Therefore followed researches are needed about inter and intra processing of team creativity.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.1-7
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• 2024

Comparing text features involves evaluating the "similarity" between texts. It is crucial to use appropriate similarity measures when comparing similarities. This study utilized various techniques to assess the similarities between newspaper articles, including deep learning and a previously proposed method: a combination of Pointwise Mutual Information (PMI) and Word Pair Matching (WPM), denoted as PMI+WPM. For performance comparison, law data from medical research in Japan were utilized as validation data in evaluating the PMI+WPM method. The distribution of similarities in text data varies depending on the evaluation technique and genre, as revealed by the comparative analysis. For newspaper data, non-deep learning methods demonstrated better similarity evaluation accuracy than deep learning methods. Additionally, evaluating similarities in law data is more challenging than in newspaper articles. Despite deep learning being the prevalent method for evaluating textual similarities, this study demonstrates that non-deep learning methods can be effective regarding Japanese-based texts.

• Educational Technology International
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• v.11 no.2
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• pp.123-148
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• 2010

Relationships between students' perception of psychological distance with online professors and their academic learning achievement and their intention to continue online learning were examined. The courses selected for this study are two online courses: 1) 'English Grammar' and 2) 'TOEIC (Test of English for International Communication) Preparation' offered by a campus-based, medium-sized university. This study employed a mixed-methods approach by conducting a survey as well as one-on-one interviews with students. Students who feel psychologically distant with the online professors show significantly lower degree of perceived learning achievement, and higher tendency not to take online courses any more. All the three scales measuring the psychological distance -mutual awareness, connectedness, and availability- with professors turned out to be significantly related with students' perceived learning achievement. According to the result of the interview data analysis, the student interviewees unanimously said that the university should limit the number of online courses that students can register in a semester to one or two courses. Most students regard low interactivity of online learning as inevitable phenomenon. There is a statistically significant difference in perceived learning achievement between the online preferred group and the offline preferred group. Also, there is a significant difference in connectedness and availability and no significant difference in the degree of mutual awareness between the online and the offline preferred group.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.659-672
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• 2005

This paper evaluates discretization of continuous variables to select relevant variables for supervised learning using mutual information. Three discretization methods, MDL, Histogram and 4-Intervals are considered. The process of discretization and variable subset selection is evaluated according to the classification accuracies with the 6 real data sets of UCI databases. Results show that 4-Interval discretization method based on quantiles, is robust and efficient for variable selection process. We also visually evaluate the appropriateness of the selected subset of variables.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.6
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• pp.63-69
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• 2018

The machine learning model can capture the dynamics of building systems with less inputs than the first principle based simulation model. The training data for developing a machine learning model are usually selected in a heuristic manner. In this study, the authors developed a machine learning model which can describe supply air temperature from an AHU in a real office building. For rational reduction of the training data, the progressive sampling method was used. It is found that even though the progressive sampling requires far less training data (n=60) than the offline regular sampling (n=1,799), the MBEs of both models are similar (2.6% vs. 5.4%). In addition, for the update of the machine learning model, the normalized mutual information (NMI) was applied. If the NMI between the simulation output and the measured data is less than 0.2, the model has to be updated. By the use of the NMI, the model can perform better prediction ($5.4%{\rightarrow}1.3%$).

• International Journal of Advanced Culture Technology
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• v.8 no.1
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• pp.44-55
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• 2020

The degree of mutual understanding between nursing students and instructors regarding simulation-based education remains unknown. The purpose of this study was to identify the subjectivity of nursing students and instructors about simulation-based learning, and was intended to expand the mutual understand by employing the co-orientation model. Q-methodology was used to identify the perspectives of 46 nursing students and 38 instructors. Perception types found among students in relation to simulation-based learning were developmental training seekers, instructor-dependent seekers, and learning achievement seekers. The instructors estimated the student perception types as passive and dependent, positive commitment, demanding role as facilitators, and psychological burden. Perception types found among instructors included nursing capacity enhancement seekers, self-reflection seekers, and reality seekers. The students classified the instructors' perception types as nursing competency seekers, learning reinforcement seekers, and debriefing-oriented seekers. As a result of the analysis of these relations in the co-orientation model, instructors identified psychological burden and passive and dependent cognitive frameworks among students; however, these were not reported in the students' perspectives. Likewise, the reality seekers type found among the perception types of instructors was not identified by the students. These findings can help develop and implement simulation-based curricula aimed at maximizing the learning effect of nursing students.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.160-165
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• 2022

Mutual distillation is a knowledge distillation method that guides a cohort of neural networks to learn cooperatively by transferring knowledge between them, without the help of a teacher network. This paper aims to confirm whether mutual distillation is also applicable to super-resolution networks. To this regard, we conduct experiments to apply mutual distillation to the discriminators of SRGANs and analyze the effect of mutual distillation on improving SRGAN's performance. As a result of the experiment, it was confirmed that SRGANs whose discriminators shared their knowledge through mutual distillation can produce super-resolution images enhanced in both quantitative and qualitative qualities.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.75-88
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• 2022

With the development of the sharing economy, existing recommender services are changing from user-item recommendations to user-user recommendations. The most important consideration is that all users should have the best possible satisfaction. To achieve this outcome, the matching service adds information between users and items necessary for the existing recommender service and information between users, so higher-level data mining is required. To this end, this paper proposes a user-to-user matching service (UTU-MS) employing the prediction of mutual satisfaction based on learning. Users were divided into consumers and suppliers, and the properties considered for recommendations were set by filtering and weighting. Based on this process, we implemented a convolutional neural network (CNN)-deep neural network (DNN)-based model that can predict each supplier's satisfaction from the consumer perspective and each consumer's satisfaction from the supplier perspective. After deriving the final mutual satisfaction using the predicted satisfaction, a top recommendation list is recommended to all users. The proposed model was applied to match guests with hosts using Airbnb data, which is a representative sharing economy platform. The proposed model is meaningful in that it has been optimized for the sharing economy and recommendations that reflect user-specific priorities.