• Journal of the Korean Academy of Esthetic Dentistry
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• v.25 no.1
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• pp.15-24
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• 2016

It is considered an implant failure when there is esthetic problems in the anterior area although the prosthesis function normally. In 2003, Dr. Kan et al stated that implant bone level is determined by the adjacent teeth. After that many scholars have studied how can achieve the esthetics result on adjacent teeth bone loss cases. In 2012, Dr. Takino published an article in Quintessence. He summarized previous articles and reclassified the defects from class 1 through 4. Class 1 and 2 depicts a situation where there is no bone loss on adjacent teeth. In Class 3 and 4, interproximal bone loss extends to the adjacent tooth. If one side is involved, it is Class 3. If both sides are involved, it is Class 4. The clue for esthetic implant restoration is whether bone loss extends to adjacent tooth or not. If the bone level of adjacent tooth is sound, we can easily achieve the esthetic but the bone level is not sound, the surgery will be complicated and the esthetic result will be unpredictable. So regenerative surgery for adjacent tooth is necessary for long-term maintenance. But the options and process were so complicated, the purpose of this article is to report the method simplify the surgery and gain a similar outcome.

• Journal of KIISE:Software and Applications
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• v.35 no.8
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• pp.463-478
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• 2008

Due to the high dimensional problem, typically machine learning algorithms have relied on feature selection techniques in order to perform effective classification in microarray gene expression datasets. However, the large number of features compared to the number of samples makes the task of feature selection computationally inprohibitive and prone to errors. One of traditional feature selection approach was feature filtering; measuring one gene per one step. Then feature filtering was an univariate approach that cannot validate multivariate correlations. In this paper, we proposed a function for measuring both class separability and correlations. With this approach, we solved the problem related to feature filtering approach.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.3
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• pp.45-64
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• 2017

The purpose of this study is to understand the direction of the community reading campaign in the U.S.A. known as 'One Book, One City' reflected in the books selected for this campaign for the past 20 years in terms of their classification numbers, subject headings, publication dates, and genres. Analyzed are the author and state lists of 'One Book, One City' Reading Promotions Projects available from the website of the LC (Library of Congress) Center for the Books, and bibliographic records of 735 books selected in only one 'One Book' program, accessed from LC OPAC. Major findings include continuing influences of the all-time favorite 'One Book' selections, including To Kill a Mockingbird and the extension of their span of life through The Big Read, preference for the recent publications, importance of P (Literatures and Languages) Class (530 titles, 72.1%) and PS(American Literatures) subclass (307 titles, 57.9%) in the LC Classification Scheme, distribution of books in 43 genres, including domestic fiction, historical fiction, and psychological fiction, etc., the use of 535 unique LC subject headings and much interests in "City and town life" (10 titles) and "World War, 1939-1945" (8 titles), and prominence of subject groups which begin with "African American..." and "Woman..." out of 96 groups of subject headings. It is found that the subjects and focus of the selected books expand from integration, understanding, integrity to human rights, environment, peace, etc. The limitations of this study is that the influence of the selected books and the changes in communities are not properly analyed.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.321-339
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• 2017

In this work, we are interested in the extraction of areas of interest from satellite images by introducing a MO-TRIBES/OC-SVM approach. The One-Class Support Vector Machine (OC-SVM) is based on the estimation of a support that includes training data. It identifies areas of interest without including other classes from the scene. We propose generating optimal training data using the Multi-Objective TRIBES (MO-TRIBES) to improve the performances of the OC-SVM. The MO-TRIBES is a parameter-free optimization technique that manages the search space in tribes composed of agents. It makes different behavioral and structural adaptations to minimize the false positive and false negative rates of the OC-SVM. We have applied our proposed approach for the extraction of earthquakes and urban areas. The experimental results and comparisons with different state-of-the-art classifiers confirm the efficiency and the robustness of the proposed approach.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.3004-3006
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• 2005

Support vector machine(SVM)은 입력 데이터를 두개의 다른 클래스로 구별하는 결정면을 학습과정을 통하여 구한다. 기존의 SVM은 단지 이차 클래스에 대하여 적용되어지나, 많은 응용분야에서 입력 데이터들은 몇 개의 다중 클래스로 분류해야 한다. 다중 클래스 분류 문제는 기존의 SVM을 사용할 수 있는 일반적으로 몇 개의 2차 문제로 분해하여 풀 수 있다. 실례로 one-against-all 방법을 적용하면, n 클래스 문제는 n 개의 두 클래스 문제로 변환 하여 풀 수 있다. 본 논문에서는 입력 패턴들을 다중 클래스로 분류 할 때 퍼지 소속도를 응용한 소프트 마진 알고리즘의 상한 경계값을 각 클래스에 따라 다르게 적용함으로써 기존의 SVM 보다 더 우수한 학습 능력을 가짐을 보였다.

• The Korean Journal of Ecology
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• v.10 no.4
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• pp.165-173
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• 1987

Odong Island, Yeosu, is the one of the Hallyosudo National Marine Park. The vegetation of this island was surveed from July, 1986 through April, 1987. By the Braun-Blanquet's method, the vegetation of Odong Island was classified into 7 communities and 4 afforestations; that is, Pseudosasa japonica community and Phyllostachys bambusoides afforestation (bamboo stands), Mallotus japonicus, Quercus acutissima community, Prunus serrulata var. spontanes and Celtis sinenesis afforestation (deciduous forests), Pinus densiflora, Pinus thunbergii community, Chamaecyparis pisifera afforestation (evergreen needle-leaved forests), and Castanopsis cuspidata var. sieboldii-Camellia japonica and Machilus thunbergii-Camellia japonica community (evergreen broad-leaved forests). Based on the classification, the actual vegetation map of the island was prepared in scale 1:2,600. Judging by the DBH class distribution and many other informations, ww can expect that the coniferous forests area of the island will be replaced by evergreen broad-lea ed forests after a few future.

• Speech Sciences
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• v.12 no.1
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• pp.123-134
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• 2005

Confidence measure (CM) is used for the rejection of mis-recognized words in an automatic speech recognition (ASR) system. Rahim, Lee, Juang and Cho's confidence measure (RLJC-CM) is one of the widely-used CMs [1]. The RLJC-CM is calculated by averaging phone-level CMs. An extension of the RLJC-CM was achieved by Kim et al [2]. They devised the normalized CM (NCM), which is a statistically normalized version of the RLJC-CM by using the tri-phone based CM normalization. In this paper we verify the NCM by generalizing tri-phone to n-phone unit. To apply various units for the normalization, mono-phone, tri-phone, quin-phone and $\infty$-phone are tested. By the experiments in the domain of the isolated word recognition we show that tri-phone based normalization is sufficient enough to enhance the rejection performance of the ASR system. Also we explain the NCM in regard to two class pattern classification problems.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.37-39
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• 2006

지지 벡터 기계(Support Vector Machine; SVM)는 기본적으로 이진분류를 위해 고안되었지만, 최근 다양한 분류기 생성전략과 결합전략이 고안되어 다중부류 분류에도 적용되고 있다. 본 논문에서는 OVR(One-Vs-Rest) 전략으로 생성된 SVM을 NB(Naive Bayes) 분류기를 이용하여 동적으로 구성함으로써, OVR SVM을 이용한 다중부류 분류 시스템에서 자주 발생하는 동점을 효과적으로 해결하는 방법은 제안한다. 이 방법을 유전발현 데이터를 이용한 다중부류 암 분류에 적용하였는데, 고차원의 데이터로부터 NB 분류기 구축에 유용한 유전자를 선택하기 위해 Pearson 상관계수를 사용하였다. 14개의 암 유형과 16,063개의 유전발현 수준을 가지는 대표적인 다중부류 암 분류 데이터인 GCM 암 데이터에 적용하여 제안하는 방법의 유용성을 확인하였다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.632-638
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• 2002

In novelty detection, one attempts to discriminate abnormal patterns from normal ones. Novelty detection is quite difficult since, unlike usual two class classification problems, only normal patterns are available for training. Auto-Associative Multi-Layer Perceptron (AAMLP) has been shown to provide a good performance based upon the property that novel patterns usually have larger auto-associative errors. In this paper, we give a mathematical analysis of 2-layer AAMLP's output characteristics and empirical results of 2-layer and 4-layer AAMLPs. Various activation functions such as linear, saturated linear and sigmoid are compared. The 2-layer AAMLPs cannot identify non-linear boundaries while the 4-layer ones can. When the data distribution is multi-modal, then an ensemble of AAMLPs, each of which is trained with pre-clustered data is required. This paper contributes to understanding of AAMLP networks and leads to practical recommendations regarding its use.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.187-190
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• 2001

Due to the recent proliferation of the internet, it is broadly granted that the necessity of the automatic document categorization has been on the rise. Since it is a heavy time-consuming work and takes too much manpower to process and classify manually, we need a system that categorizes them automatically as their contents. In this paper, we propose the automatic E-mail response system that is based on 2 hierarchical document clustering methods. One is to get the final result from the classifier trained seperatly within each class, after clustering the whole documents into 3 groups so that the first classifier categorize the input documents as the corresponding group. The other method is that the system classifies the most distinct classes first as their similarity, successively. Neural networks have been adopted as classifiers, we have used dendrograms to show the hierarchical aspect of similarities between classes. The comparison among the performances of hierarchical and non-hierarchical classifiers tells us clustering methods have provided the classification efficiency.