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Frequentist and Bayesian Learning Approaches to Artificial Intelligence
Sunghae Jun 한국지능시스템학회 2016 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.16 No.2
Artificial intelligence (AI) is making computer systems intelligent to do right thing. The AI is used today in a variety of fields, such as journalism, medical, industry as well as entertainment. The impact of AI is becoming larger day after day. In general, the AI system has to lead the optimal decision under uncertainty. But it is difficult for the AI system can derive the best conclusion. In addition, we have a trouble to represent the intelligent capacity of AI in numeric values. Statistics has the ability to quantify the uncertainty by two approaches of frequentist and Bayesian. So in this paper, we propose a methodology of the connection between statistics and AI efficiently. We compute a fixed value for estimating the population parameter using the frequentist learning. Also we find a probability distribution to estimate the parameter of conceptual population using Bayesian learning. To show how our proposed research could be applied to practical domain, we collect the patent big data related to Apple company, and we make the AI more intelligent to understand Apple’s technology.
Empirical Comparisons of Clustering Algorithms using Silhouette Information
Sunghae Jun,Seung-Joo Lee 한국지능시스템학회 2010 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.10 No.1
Many clustering algorithms have been used in diverse fields. When we need to group given data set into clusters, many clustering algorithms based on similarity or distance measures are considered. Most clustering works have been based on hierarchical and non-hierarchical clustering algorithms. Generally, for the clustering works, researchers have used clustering algorithms case by case from these algorithms. Also they have to determine proper clustering methods subjectively by their prior knowledge. In this paper, to solve the subjective problem of clustering we make empirical comparisons of popular clustering algorithms which are hierarchical and non hierarchical techniques using Silhouette measure. We use silhouette information to evaluate the clustering results such as the number of clusters and cluster variance. We verify our comparison study by experimental results using data sets from UCI machine learning repository. Therefore we are able to use efficient and objective clustering algorithms.
How is Youth`s Emotion Cultivated in Dance Education? : An Exploratory Study
( Sunghae Park ) 한국체육학회 2016 국제스포츠과학 학술대회 Vol.2016 No.1
Purpose: As emotional problems of youth are getting serious, there has been a growing interest in developing youth’s emotion. However, most of research and programs have focused on the cognitive aspects of emotion and dealt with special populations of youth with behavior problems. In this context, this study is an exploratory study investigating the cultivation of emotion in general youth populations. The purposes of this study are to identify positive emotional experiences of youth in dance education and explore the factors that contribute to the cultivation of youth’s emotion. Research questions are as follows: (a) What are the positive emotional experiences of youth in dance education?, (b) What are the factors that have a positive impact on youth’s emotion? Method: Qualitative research method was conducted for this study. Participants were 16 to 22 years old students (n=20, 9 males and 11 females) who were engaged in dance education lessons. Data were collected by open-ended questionnaires, non-participant observations, and in-depth interviews. Data were analyzed inductively and trustworthiness of data was enhanced through member checks and peer debriefing. Result: Two aspects of youth’s emotional experiences . personal and interpersonal emotions - were classified. The personal emotion included an immediate and instinctive sense of feelings (e.g. pleasure, excitement, and happiness, etc.) and complicated combination of those feelings (e.g. a sense of fulfillment, confidence, and self-examination, etc.). The interpersonal emotion was shown in terms of various layers from empathy with others to a sense of consideration, faith, and duty toward others. There were three main factors promoting youth’s emotional cultivation. Diverse narrative contents, songs, movies, readings, writings, and discussions, with dance techniques enriched youth’s emotional experiences. In addition, appropriate teachers’ indirect teaching behavior and positive interactions with classmates played a crucial role in contributing to the cultivation of youth’s emotion. Conclusion: Dance education can be seen as essential for cultivating youth’s emotion. Furthermore, youth’s emotional cultivation (e.g. caring, cooperation, and responsibility) is closed to character development. For the further research, it will be needed to study the roles of teachers and foundations of dance education programs in order to maximize positive youth’s emotional experiences.