• Journal of Korean Home Economics Education Association
• /
• v.26 no.3
• /
• pp.113-131
• /
• 2014

This study aimed to explore the effects of private tutoring expenses, parents' monitoring affection, their children's learning value and self-regulated learning abilities on middle-school boys' and girls' English Math academic achievement. The subjects were the 3rd middle-school 1,123 students taking the private tutoring of English and Math who participated in the Korea Child Youth Panel Surveys(KCYPS). The data were analyzed with descriptive statistics, correlations and hierarchical regressions. The main results of this study were as follows. Firstly, regardless of middle-school students' sex, as monthly average private tutoring expenses were more, the levels of parents' monitoring, and their children's learning value self-regulated learning abilities were higher, so middle-school students' academic achievement was higher. Secondly, regardless of middle-school students' sex, their self-regulated learning abilities were the highest predictors of English Math achievement. Also, their learning value and parents' monitoring influenced middle-school boys' English Math achievement in order. On the other hand, monthly average private tutoring expenses influenced middle-school girls' English Math achievement. Furthermore there were no moderating effects of parents' monitoring affection, their children's learning value and self-regulated learning abilities between monthly average private tutoring expenses and middle-school boys' and girls' English Math achievement. Finally, based on the results, the importance of parents and Home Economics was suggested in attaining middle-school students' higher academic achievement. Especially, Home Economics can play an important role of enhancing middle-school students' self-regulated learning abilities and learning value necessary for middle-school students' higher academic achievement.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2002.11b
• /
• pp.869-872
• /
• 2002

네트워크 기반 침입탐지시스템은 연속적으로 발생하는 패킷의 무손실 축소와, 패킷으로 정상 또는 비정상 행위패턴을 정확히 모델링한 모델 생성이 전체성능을 판단하는 중요한 요소가 된다. 네트워크 기반 비정상행위 판정 침입탐지시스템에서는 이러한 탐지모델 구축을 위해 주로 감독학습 알고리즘을 사용한다. 본 논문은 탐지모델 구축에 사용하는 감독 학습 방식이 가지는 문제점을 지적하고, 그에 대한 대안으로 비감독 학습방식의 학습알고리즘을 제안한다. 감독 학습을 사용하여 탐지모델을 구축하기 위해서는 정상행위의 패킷을 취합해야 하는 사전 부담이 있는 반면에 비감독 학습을 사용하게 되면 이러한 사전작업 없이 탐지모델을 구축할 수 있다. 본 논문에서는 비감독학습 알고리즘을 비교 분석하기 위해서 COBWEB, k-means, Autoclass 알고리즘을 사용했으며, 성능을 평가하기 위해서 비정상행위도(Abnormal Behavior Level)를 계산하여 에러율을 구하였다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2007.06c
• /
• pp.303-308
• /
• 2007

최근에 제안된 감독 지식을 융합하는 강화 학습 기법인 potential-based RL 기법의 효용성은 이론적 최적 정책으로의 수렴성 보장으로 증명되었고, policy-reuse RL 기법의 우수성은 감독지식을 융합하지 않는 기존의 강화학습과 실험적인 비교를 통하여 증명되었지만, policy-reuse RL 기법을 potential-based RL 기법과 비교한 연구는 아직까지 제시된 바가 없었다. 본 논문에서는 potential-based RL 기법과 policy-reuse RL 기법의 실험적인 성능 비교를 통하여 기법이 policy-reuse RL 기법이 policy-reuse RL 기법에 비하여 더 빠르게 수렴한다는 것을 보이며, 또한 policy-reuse RL 기법의 성능은 재사용하는 정책의 optimality에 영향을 받는다는 것을 보인다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2013.11a
• /
• pp.945-947
• /
• 2013

입력 모듈의 결함경향성을 결정하는 결함 예측 모델 연구들은 대부분 훈련 데이터 집합을 사용하는 감독형 모델에 관련된 것들이었다. 하지만 과거 데이터 집합이 없거나 현재 프로젝트 성격이 다른 경우는 비감독형 모델이 필요하며, 이들에 관한 연구들은 모델 구축의 어려움 때문에 극소수 존재한다. 본 논문에서는 대표적인 클러스터링 알고리즘들을 사용한 비감독형 모델들을 제작하여, 기존 모델들이 많이 사용한 K-means 모델과 나머지 모델들의 성능을 비교하였다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.04b
• /
• pp.646-648
• /
• 2004

기계학습의 군집화(clustering) 기법은 예제들 간의 유사성에 근거하여 주어진 예제들을 무리 짓는 방법이다. 준감독(semi-supervised) 군집화는 카테고리가 부여된(labeled) 소수의 예제들을 적극적으로 활용하여 군집형태가 보다 자연스럽게 형성되도록 유도하는 군집화 방법이다. 준감독 군집화 문제에서 예제에 카테고리를 부여하는 작업은 현실적으로 극히 제한적이거나 카테고리를 부여하는데 소요되는 비용이 상당하므로, 제한된 자원 내에서 군집화에 효용성이 높을 예제들을 선정하여 카테고리를 부여하는 것이 필요하다. 본 논문에서는 기존 연구에서 능동적 학습의 초기 훈련예제 선정을 위해 제안된 군집기반 훈련예제 선정 방법을 준감독 군집화에 적용하여 군집 결과의 질을 향상시키고자 한다. 군집화를 이용한 예제 선정 방법은 유사한 예제들은 동일한 카테고리에 속할 가능성이 높다는 가정하에 전체 예제를 활용하여 선정하고자 하는 예제 수만큼 군집을 생성 한 후. 각 군집의 중심점에 가장 가까운 예제들을 대표 예제로 선정하여 훈련 집합을 구성하는 방법이다 본 논문에서는 문서를 대상으로 하는 준감독 군집화 실험을 통해, 카테고리를 부여할 예제를 임의로 선정한 경우에 비해 군집화를 이용한 훈련 예제들로 준감독 군집화를 수행한 경우가 보다 좋은 군집을 형성함을 확인하였다.

• 한국체육학회지인문사회과학편
• /
• v.54 no.4
• /
• pp.329-345
• /
• 2015

Our study examines the relationship between coach turnover and professional sport team performance using the evidences of Korean professional soccer teams. We collected panel dataset of 304 team-year observations and 96 coaches from Korean professional soccer league during the period of 1983-2013. Among them, our final sample is comprised of 226 observations and 81 coaches manifested for fixed-effect generalized least square (GLS) regression analysis. Drawing on sport management literatures and organizational learning theory, we argue that it takes time for a new head coach to take charge of the team in which the new leader who secure more time to interact with organization members is better able to remodel and improve team performance. Our empirical findings reveal that off-season coach turnover has a positive impact while turnover during the season has its negative influences on team performance. In addition, we find that subsequent team performance in association of off-season coach turnover is comparably better than that of on-season succession. The results show that coach succession rendered from inside the professional soccer team mediates the relationship between coach turnover and team performance. Our findings imply that coach turnover in professional sport teams is a significant factor affecting team performance.

• KIPS Transactions on Software and Data Engineering
• /
• v.3 no.2
• /
• pp.57-64
• /
• 2014

Most previous studies of software fault prediction model which determines the fault-proneness of input modules have focused on supervised learning model using training data set. However, Unsupervised learning model is needed in case supervised learning model cannot be applied: either past training data set is not present or even though there exists data set, current project type is changed. Building an unsupervised learning model is extremely difficult that is why only a few studies exist. In this paper, we build unsupervised models using representative clustering algorithms, EM and DBSCAN, that have not been used in prior studies and compare these models with the previous model using K-means algorithm. The results of our study show that the EM model performs slightly better than the K-means model in terms of error rate and these two models significantly outperform the DBSCAN model.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.3
• /
• pp.151-157
• /
• 2018

Most previous studies of software fault prediction have focused on supervised learning models for binary classification that determines whether an input module has faults or not. However, binary classification model determines only the presence or absence of faults in the module without considering the complex characteristics of the fault, and supervised model has the limitation that it requires a training data set that most development groups do not have. To solve these two problems, this paper proposes severity-based ternary classification model using unsupervised learning algorithms, and experimental results show that the proposed model has comparable performance to the supervised models.

• Journal of KIISE:Computing Practices and Letters
• /
• v.14 no.5
• /
• pp.502-506
• /
• 2008

The recently proposed "Potential-based" reinforcement learning (RL) method made it possible to combine multiple learnings and expert advices as supervised knowledge within an RL framework. The effectiveness of the approach has been established by a theoretical convergence guarantee to an optimal policy. In this paper, the potential-based RL method is applied to a dynamic channel assignment (DCA) problem in a cellular networks. It is empirically shown that the potential-based RL assigns channels more efficiently than fixed channel assignment, Maxavail, and Q-learning-based DCA, and it converges to an optimal policy more rapidly than other RL algorithms, SARSA(0) and PRQ-learning.

• Korean Journal of Remote Sensing
• /
• v.33 no.5_2
• /
• pp.689-698
• /
• 2017

In this paper, a new classification method based on the combination of semi-supervised learning with spatial similarity of adjacent pixels is presented for crop classification in inaccessible areas. Iterative classification based on semi-supervised learning is applied to extract reliable training data from both the initial classification result with a small number of training data, and classification results of adjacent pixels are also considered to extract new training pixels with less uncertainty. To evaluate the applicability of the proposed method, a case study of the classification of field crops was carried out using multi-temporal Landsat-8 OLI acquired in the Daehongdan region, North Korea. From a case study, the misclassification of crops and forests, and isolated pixels in the initial classification result were greatly reduced by applying the proposed semi-supervised learning method. In addition, the combination of classification results of adjacent pixels for the extraction of new training data led to the great reduction of both misclassification results and isolated pixels, compared to the initial classification and traditional semi-supervised learning results. Therefore, it is expected that the proposed method would be effectively applied to classify areas in which it is difficult to collect sufficient training data.