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Conceptual Differences between the Relation-Based Approach and the Feature-Based Approach in Noun-Noun Conceptual Combination (개념결합 처리과정에 대한 관계 - 기반 접근과 차원- 기반 접근의 조망 차이)

  • Choi, Min-Gyung;Shin, Hyun-Jung
    • Korean Journal of Cognitive Science
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    • v.21 no.1
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    • pp.199-231
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    • 2010
  • This study tried to contrast the relation-based and the dimension-based explanations and to suggest its implications on the noun-noun conceptual combination. In experiment 1, we investigated whether the dimension-based approach and intra-conceptual explanation can explain both thematic relational and property interpretations of conceptual combinations based upon the intrinsic and extrinsic features of constituent concepts. We defined intrinsic(or extrinsic) concepts according to the degree of dependency on intrinsic(or extrinsic) features. Property interpretation was facilitated when modifiers were the intrinsic concepts. This result implies that processing of conceptual combination can be influenced by the structures and information of constituent concepts. In experiment 2, exocentricity of the concepts used in Gagne(2000) was examined to reanalyze her data according to the dimension-based approach. The exocentricity was higher when the concepts were combined by their relational connections. Results of experiment 1 and 2 suggest the possibility that both approaches can be integrated through the diversities of information involved during interpreting conceptual combination. Implications and future directions of this study were discussed.

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A study on the comparison of descriptive variables reduction methods in decision tree induction: A case of prediction models of pension insurance in life insurance company (생명보험사의 개인연금 보험예측 사례를 통해서 본 의사결정나무 분석의 설명변수 축소에 관한 비교 연구)

  • Lee, Yong-Goo;Hur, Joon
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.1
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    • pp.179-190
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    • 2009
  • In the financial industry, the decision tree algorithm has been widely used for classification analysis. In this case one of the major difficulties is that there are so many explanatory variables to be considered for modeling. So we do need to find effective method for reducing the number of explanatory variables under condition that the modeling results are not affected seriously. In this research, we try to compare the various variable reducing methods and to find the best method based on the modeling accuracy for the tree algorithm. We applied the methods on the pension insurance of a insurance company for getting empirical results. As a result, we found that selecting variables by using the sensitivity analysis of neural network method is the most effective method for reducing the number of variables while keeping the accuracy.

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Explanation of Run Productivity Using Weighted Adjusted OPS in Korean Professional Baseball (한국 프로야구에서 가중수정OPS를 이용한 득점력 설명)

  • Kim, Hyuk Joo;Kim, Yea Hyoung
    • The Korean Journal of Applied Statistics
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    • v.27 no.5
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    • pp.731-741
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    • 2014
  • We suggested an adjusted OPS and weighted adjusted OPS as indices to explain run productivity of teams using the slugging average and adjusted OBP from Korean professional baseball. First, we defined adjusted OBP by modifying currently used OBP. Next, we defined adjusted OPS as the sum of adjusted OBP and slugging average. We also defined weighted adjusted OPS as the weighted average of adjusted OBP and slugging average. Analysis of the data from all games in the regular seasons from 1982~2013 shows that adjusted OPS better explains runs than OPS. For 25 seasons out of 32 seasons, adjusted OPS explains runs better than OPS. Further, weighted adjusted OPS consisting of adjusted OBP (with weight 60%) and slugging average (with weight 40%) gives the best explanation of run productivity. Weighted adjusted OPS has been found to explain run productivity better than weighted OPS proposed in Kim (2012).

The Development of the Korean Medicine Symptom Diagnosis System Using Morphological Analysis to Refine Difficult Medical Terminology (전문용어 정제를 위한 형태소 분석을 이용한 한의학 증상 진단 시스템 개발)

  • Lee, Sang-Baek;Son, Yun-Hee;Jang, Hyun-Chul;Lee, Kyu-Chul
    • KIISE Transactions on Computing Practices
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    • v.22 no.2
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    • pp.77-82
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    • 2016
  • This paper presents the development of the Korean medicine symptom diagnosis system. In the Korean medicine symptom diagnosis system, the patient explains their symptoms and an oriental doctor makes a diagnosis based on the symptoms. Natural language processing is required to make a diagnosis automatically through the patients' reports of symptoms. We use morphological analysis to get understandable information from the natural language itself. We developed a diagnosis system that consists of NoSQL document-oriented databases-MongoDB. NoSQL has better performance at unstructured and semi-structured data, rather than using Relational Databases. We collect patient symptom reports in MongoDB to refine difficult medical terminology and provide understandable terminology to patients.

A credit classification method based on generalized additive models using factor scores of mixtures of common factor analyzers (공통요인분석자혼합모형의 요인점수를 이용한 일반화가법모형 기반 신용평가)

  • Lim, Su-Yeol;Baek, Jang-Sun
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.2
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    • pp.235-245
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    • 2012
  • Logistic discrimination is an useful statistical technique for quantitative analysis of financial service industry. Especially it is not only easy to be implemented, but also has good classification rate. Generalized additive model is useful for credit scoring since it has the same advantages of logistic discrimination as well as accounting ability for the nonlinear effects of the explanatory variables. It may, however, need too many additive terms in the model when the number of explanatory variables is very large and there may exist dependencies among the variables. Mixtures of factor analyzers can be used for dimension reduction of high-dimensional feature. This study proposes to use the low-dimensional factor scores of mixtures of factor analyzers as the new features in the generalized additive model. Its application is demonstrated in the classification of some real credit scoring data. The comparison of correct classification rates of competing techniques shows the superiority of the generalized additive model using factor scores.

Promotion and Wage in the Internal Labour Market : Sexual Differences (기업내부노동시장의 승진과 임금: 성별 차이를 중심으로)

  • 금재호
    • Korea journal of population studies
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    • v.25 no.1
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    • pp.181-211
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    • 2002
  • Using the fourth data of the Korea Labor and Income Panel Study (KLIPS), this paper analyzed sexual differences in the promotion possibility and the promotion experience. Effects on wage of the promotion possibility and the promotion experience have been also discussed in detail. The promotion probability of a male worker in his current job is as high as twice than that of a female worker after controlling other independent variables. However, if we restrict the analysis to workers who either can be or was promoted, the sexual difference in the promotion possibility is greatly narrowed. This result suggests that the continuous career development without disruption is critical for the promotion of female workers. Analysing the sexual difference in wage using Oaxaca and Ransom's methodology, explanatory variables, such as human capital, residential area, etc., explained 69.5% of wage difference between male and female workers. Especially, 13.9% of wage difference was contributed to sexual differences in the promotion possibility and the promotion experience. This kind of empirical result emphasized once again the importance of promotion on wage.

Comments on the regression coefficients (다중회귀에서 회귀계수 추정량의 특성)

  • Kahng, Myung-Wook
    • The Korean Journal of Applied Statistics
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    • v.34 no.4
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    • pp.589-597
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    • 2021
  • In simple and multiple regression, there is a difference in the meaning of regression coefficients, and not only are the estimates of regression coefficients different, but they also have different signs. Understanding the relative contribution of explanatory variables in a regression model is an important part of regression analysis. In a standardized regression model, the regression coefficient can be interpreted as the change in the response variable with respect to the standard deviation when the explanatory variable increases by the standard deviation in a situation where the values of the explanatory variables other than the corresponding explanatory variable are fixed. However, the size of the standardized regression coefficient is not a proper measure of the relative importance of each explanatory variable. In this paper, the estimator of the regression coefficient in multiple regression is expressed as a function of the correlation coefficient and the coefficient of determination. Furthermore, it is considered in terms of the effect of an additional explanatory variable and additional increase in the coefficient of determination. We also explore the relationship between estimates of regression coefficients and correlation coefficients in various plots. These results are specifically applied when there are two explanatory variables.

전자제품의 고장 메커니즘

  • 김진우
    • Journal of the KSME
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    • v.43 no.6
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    • pp.55-57
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    • 2003
  • 이 글에서는 전자제품의 고장 메커니즘의 개념에 대하여 설명하고, 고장 발생 유형에 다른 분류에 대해 설명한다.

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적응 제어의 소개

  • 최종호
    • 전기의세계
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    • v.30 no.12
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    • pp.770-772
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    • 1981
  • 적응 제어의 이론을 설명하기 위하여 STR 방법과 MARC방법에 대하여 각각 하나의 방법을 소개하였고 이들을 사용하는데 고려하여야 할 점들을 기술하였다.

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자동차 산업의 CALS추진 현황

  • 이남희
    • CDE review
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    • v.6 no.1
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    • pp.37-44
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    • 2000
  • 자동차 산업의 CALS추진 현황을 설명하기 앞서 간단하게 CALS의 개요를 알아보고 현재 추진 중인 자동차 CALS 선행연구 사업의 내용을 소개하기로 한다.

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