• Animal cells and systems
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• v.9 no.2
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• pp.47-55
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• 2005

Avian vocalization has been main topics in studying animal communication. The structure and usage as well as development and function of vocalization vary enormously among species and even among populations, and thus we reviewed the general patterns of song learning and the consequences of song communication in birds at the behavioural level: first, we compared the different learning phenomena between non-songbird and songbird, and we investigated the learning process of songbird both in the field and in the lab, which are needed to fully understand vocal communication. Second, we discussed a recent trend of sexual selection hypothesis explaining the structural and functional diversity of song in songbirds with repertoire and presented how the repertoire is actually used between neighbours based on individual recognition.

• Journal of Industrial Convergence
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• v.7 no.2
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• pp.45-57
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• 2009

I first present a set of features for distinguishing group learning from other concepts. I then develop a framework for understanding group learning that focuses on learning's basic processes at the group level of analysis: sharing.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.14-21
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• 2005

Even though mainly statistical methods have been used in anomaly network intrusion detection, to detect various attack types, machine learning based anomaly detection was introduced. Machine learning based anomaly detection started from research applying traditional learning algorithms of artificial intelligence to intrusion detection. However, detection rates of these methods are not satisfactory. Especially, high false positive and repeated alarms about the same attack are problems. The main reason for this is that one packet is used as a basic learning unit. Most attacks consist of more than one packet. In addition, an attack does not lead to a consecutive packet stream. Therefore, with grouping of related packets, a new approach of group-based learning and detection is needed. This type of approach is similar to that of multiple-instance problems in the artificial intelligence community, which cannot clearly classify one instance, but classification of a group is possible. We suggest group generation algorithm grouping related packets, and a learning algorithm based on a unit of such group. To verify the usefulness of the suggested algorithm, 1998 DARPA data was used and the results show that our approach is quite useful.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.113-120
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• 2022

Mongolian folk songs are inspired by Mongolian labor songs and are classified into long and short songs. Mongolian long songs have ancient origins, are rich in legends, and are a great source of folklore. So it was inscribed by UNESCO in 2008. Mongolian written literature is formed under the direct influence of oral literature. Mongolian long song has 3 classes: ayzam, suman, and besreg by their lyrics and structure. In ayzam long song, the world perfectly embodies the philosophical nature of world phenomena and the nature of human life. Suman long song has a wide range of topics such as the common way of life, respect for ancestors, respect for fathers, respect for mountains and water, livestock and animal husbandry, as well as the history of Mongolia. Besreg long songs are dominated by commanded and trained characters. In this paper, we proposed a method to classify their 3 types of long songs using machine learning, based on their lyrics structures without semantic information. We collected lyrics of over 80 long songs and extracted 11 features from every single song. The features are the name of a song, number of the verse, number of lines, number of words, general value, double value, elapsed time of verse, elapsed time of 5 words, and the longest elapsed time of 1 word, full text, and type label. In experimental results, our proposed features show on average 78% recognition rates in function type machine learning methods, to classify the ayzam, suman, and besreg classes.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.572-587
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• 2020

Experience API provides a learner-centered model for learning data collection and learning process recording. In particular, it can record learning data from multiple data sources. Therefore, Experience API provides very good support for ubiquitous learning. In this paper, we put forward the architecture of ubiquitous learning system and the method of reading the learning record from the ubiquitous learning system. We analyze students' learning behavior from two aspects: horizontal and vertical, and give the analysis results. The system can provide personalized suggestions for learners according to the results of learning analysis. According to the feedback from learners, we can see that this u-learning system can greatly improve learning interest and quality of learners.

• Journal of Korea Multimedia Society
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• v.9 no.6
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• pp.762-772
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• 2006

Instance based learning algorithm is the best known lazy learner and has been successfully used in many areas such as pattern analysis, medical analysis, bioinformatics and internet applications. However, its feature weighting scheme is too naive that many other extensions are proposed. Our version of IB3 named as eXtended IBL (XIBL) improves feature weighting scheme by backward stepwise regression and its distance function by VDM family that avoids overestimating discrete valued attributes. Also, XIBL adopts leave-one-out as its noise filtering scheme. Experiments with common artificial domains show that XIBL is better than the original IBL in terms of accuracy and noise tolerance. XIBL is applied to two important applications - intrusion detection and spam mail filtering and the results are promising.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1950-1955
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• 1991

A fuzzy dynamic learning controller is proposed and applied to control of time delayed, non-linear and unstable chemical processes. The proposed fuzzy dynamic learning controller can self-adjust its fuzzy control rules using the external dynamic information from the process during on-line control and it can create th,, new fuzzy control rules autonomously using its learning capability from past control trends. The proposed controller shows better performance than the conventional fuzzy logic controller and the fuzzy self organizing controller.

• The Journal of Korean Association of Computer Education
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• v.3 no.1
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• pp.51-63
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• 2000

All learners can selection of their work through hypermedia technology in the area provided by usual WBI. Also, it provides learner with individual teaching-learning environment and estimation. RC2 System has the fundamental client/server model, and provides the learning, evaluation algorithms based on the LCPG(Learning Contents Problem Graph) model, the dynamic re-learning mechanism in according to the property of individual. Moreover, it support learning editor to provide interface, which is convenient for teacher, Courseware writer, on the Web

• Journal of Korean Academy of Nursing Administration
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• v.16 no.3
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• pp.267-275
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• 2010

Purpose: This study conducted to identify the effect of learning organization construction and learning orientation on organizational effectiveness among hospital nurses. Method: Data was collected from convenient sample of 296 nurses who worked for the major hospitals in Seoul, Gyeonggi-do and Gangwoen-do. The self-reported questionnaire was used to assess the general characteristics, the level of the learning organization construction, learning orientation and organizational effectiveness. The data were analyzed using descriptive statistics, pearson's correlation coefficient and multiple regression. Result: The mean score of learning organization construction was 3.61(${\pm}.32$), learning orientation got 3.26(${\pm}.39$), and organizational effectiveness obtained 3.38(${\pm}.42$). The learning organization construction affects of organizational effectiveness by 44.18% and learning orientation by 37.43%. Conclusion: This finding indicates that learning organization construction and learning orientation affects the nurses' organizational effectiveness in hospital.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.215-226
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• 2023

Deep learning has made great strides in the field of unstructured data such as text, images, and audio. However, in the case of tabular data analysis, machine learning algorithms such as ensemble methods are still better than deep learning. To keep up with the performance of machine learning algorithms with good predictive power, several deep learning methods for tabular data have been proposed recently. In this paper, we review the latest deep learning models for tabular data and compare the performances of these models using several datasets. In addition, we also compare the latest boosting methods to these deep learning methods and suggest the guidelines to the users, who analyze tabular datasets. In regression, machine learning methods are better than deep learning methods. But for the classification problems, deep learning methods perform better than the machine learning methods in some cases.