• Communications for Statistical Applications and Methods
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• v.20 no.3
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• pp.225-233
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• 2013

The linear support vector machine(SVM) is motivated by the maximal margin separating hyperplane and is a popular tool for binary classification tasks. Many studies exist on the consistency properties of SVM; however, it is unknown whether the linear SVM is consistent for estimating the optimal classification boundary even in the simple case of two Gaussian classes with a common covariance, where the optimal classification boundary is linear. In this paper we show that the linear SVM can be inconsistent in the univariate Gaussian classification problem with a common variance, even when the best tuning parameter is used.

• Journal of Biomedical Engineering Research
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• v.15 no.2
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• pp.159-166
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• 1994

A new pattern classification algorithm using cepstrum to analyze lung sounds for the classification of pattern with pulmonary and bronchial disorders is proposed. To evaluate the perfomance of the proposed method, the results are compared to the pattern classification with the AR modeling method. In the experiment lung sounds recorded for the training of physician used. As a results, the accuracy of the cepstrum classification is 92.3 % and AR modeling is the 53.8 %, therefore cepstrum modeling method has very high performance than AR and it turned out to be a very efficient algorithm.

• Journal of the Korean Society for information Management
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• v.25 no.4
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• pp.309-326
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• 2008

NTIS(National Science & Technology Information Service) provides the information for domestic research projects. It in particular has several classification schemes to classify research projects and provide better retrieval and analysis services. It however is difficult to understand the characteristic of a research project clearly since only a classification in a classification scheme can be chosen about a research project. Moreover, the classification scheme covers the high-level classification for every research areas so that it cannot cover the area specialized to the oriental medicines. On the other hand, the classification schemes for oriental medicines have recently been studied in oriental medicine field. However, it also covers the high-level classification for oriental medicine so that it may not suit to a classification scheme for research projects. Therefore, in this paper we propose a classification scheme to understand clearly the characteristic of research projects in oriental medicine and use to use them to retrieval and analysis services.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.20 no.3
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• pp.219-232
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• 2009

This paper is intended to comparatively analyzed the KDC/NDC/DDC classification system for the field of civil engineering, the research field classification system of National Research Foundation of Korea, and the science and technology research field classification system of Korea Science and Engineering Foundation. And based on the analysis, it tried to propose the ways of improving the KDC classification system for the civil engineering field. As a result of the analysis, this paper has found that the KDC 5th-edition for the civil engineering field needed some corrections. That is, the classification items that reflect the trend of academic development should be added, the classification terminology of the basic theories of civil engineering should be properly developed, segmented topics should be added, any errors in classification codes and Korean/English descriptions should be corrected, and the omission of the KDC relative index of classification items should be solved. This paper proposed the ways of improving those problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1869-1886
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• 2018

This paper proposes a MIDI- and audio-based music genre classification method for Korean traditional music. There are many traditional instruments in Korea, and most of the traditional songs played using the instruments have similar patterns and rhythms. Although music information processing such as music genre classification and audio melody extraction have been studied, most studies have focused on pop, jazz, rock, and other universal genres. There are few studies on Korean traditional music because of the lack of datasets. This paper analyzes raw audio and MIDI phrases in Korean traditional music, performed using Korean traditional musical instruments. The classified samples and MIDI, based on our classification system, will be used to construct a database or to implement our Kontakt-based instrument library. Thus, we can construct a management system for a Korean traditional music library using this classification system. Appropriate feature sets for raw audio and MIDI phrases are proposed and the classification results-based on machine learning algorithms such as support vector machine, multi-layer perception, decision tree, and random forest-are outlined in this paper.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.69-78
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• 2010

Multi-criteria ABC inventory classification is one of the most widely employed techniques for efficient inventory control, and it considers more than one criterion for categorizing inventory items into groups of different importance. Recently, Ramanathan (2006) proposed a weighted linear optimization (WLO) model for the problem of multi-criteria ABC inventory classification. The WLO model generates a set of criteria weights for each item and assigns a normalized score to each item for ABC analysis. Although the WLO model is considered to have many advantages, it has a limitation that many items can share the same optimal efficiency score. This limitation can hinder a precise classification of inventory items. To overcome this deficiency, we propose a context-dependent DEA based method for multi-criteria ABC inventory classification problems. In the proposed model, items are first stratified into several efficiency levels, and then the relative attractiveness of each item is measured with respect to less efficient ones. Based on this attractiveness measure, items can be further discriminated in terms of their importance. By a comparative study between the proposed model and the WLO model, we argue that the proposed model can provide a more reasonable and accurate classification of inventory items.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.215-224
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• 2007

The classification of the satellite images is basic part in Remote sensing. In classification of the satellite images, class separability feature is very effective accuracy of the images classified. For improving classification accuracy, It is necessary to study classification methode than analysis of class separability feature deciding classification probability. In this study, IKONOS, SPOT 5, Landsat TM, were resampled to sizes 1m grid. Above images were calculated the class separability prior to the step for classification of pixels. This Study concludes, each image was measured by the rate of class separability, values classified were showed highly about $1,600{\sim}2,000$.

• Journal of Korean Society of Archives and Records Management
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• v.12 no.3
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• pp.203-232
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• 2012

The study aims at reviewing research trends of records classification and archival arrangement, analysing issues, and suggesting futures tasks in related research area. It starts with defining research categories of records and archives classification through analysing the term of 'classification' being found in the Public Records Management Act. Thirty five research papers which are covering classification of records and archives and published in 1980's are selected for contents analysis. Considering the analysis of domestic and foreign research, and the practical needs, it makes clear the issues and tasks for future research. The study concludes with emphasizing more empirical research for guiding records and archives management and reformulating archival theories in electronic environments.

• Journal of Korea Multimedia Society
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• v.14 no.8
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• pp.1010-1019
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• 2011

This was a preliminary research for the goal of understanding between internal structure of Osteogenesis Imperfecta Murine (OIM) bone and its fragility. 54 hyperspectral bone data sets were captured by using JASCO 2000 Raman spectrometer at UMKC-CRISP (University of Missouri-Kansas City Center for Research on Interfacial Structure and Properties). Each data set consists of 1,091 data points from 9 OIM bones. The original captured hyperspectral data sets were noisy and base-lined ones. We removed the noise and corrected the base-lined data for the final efficient classification. High dimensional Raman hyperspectral data on OIM bones was reduced by Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) and efficiently classified for the first time. We confirmed OIM bones could be classified such as strong, middle and weak one by using the coefficients of their PCA or LDA. Through experiment, we investigated the efficiency of classification on the reduced OIM bone data by the Bayesian classifier and K -Nearest Neighbor (K-NN) classifier. As the experimental result, the case of LDA reduction showed higher classification performance than that of PCA reduction in the two classifiers. K-NN classifier represented better classification rate, compared with Bayesian classifier. The classification performance of K-NN was about 92.6% in case of LDA.

• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1584-1595
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• 2017

Each cultural community has developed a variety of fonts to express their own language and characters. Hangul has also diversified its font shapes through changing the composition ratio and look of the consonants and vowels. Rather, thanks to the variety of these fonts, a considerable amount of time and effort must be devoted to the selection of a specific font shape. This is related to the fact that the current Hangul service and classification system process the font only with its name or the name of the manufacturer. It means that there is no consensus about the font shape classification system for Hangul. In this study, we propose a shape property set that can be a basis for classifying Hangul fonts. The font shape property set was generated by performing statistical analysis with features which have been studied by the font design experts and was verified through questionnaire using representative fonts based on the classification scheme defined by the Hangul font design classification system standard. This study is meaningful in that it is a study on shape classification properties of K-means and PCA statistical techniques based on font data rather than design field study.