Myung-Gwan Kim

Infrared study of giant dielectric constant in Li- and Ti-doped NiO

We have measured optical reflectivity of Li and Ti doped NiO (LTNO) in the infrared range at various temperatures. A Drude-like absorption is found at low energy,

A Study on Sex Classification of a Name using Naive Bayesian

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An Implementation of Story Path Recommendation System of Interactive Drama Using PCA and NMF

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An Implementation of Pan-So-Ri Classification Program Using Naive Bayesian Classifier

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The Study on Evaluation of Team Grouping Method using Cooperative Education Program

and its spectral weight increases substantially as temperature decreases. This observation and DC-resistivity result show that LTNO has a conductive grain and resistive boundary. Such composite structure provides evidence of the Maxwell-Wagner (MW) mechanism as the origin of the high dielectric constant

A Study of Automatic Ontology Building by Web Information Extraction and Natural Language Processing

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A Study on Consumer Preferences for Partnership Model of Smartphone App

. We propose a three-phase granular structure and show that this extended MW model explains the observed frequency and temperature dependence of the dielectric constant as well as the giant value of

An Implementation of Pan-So-Ri Classification Program Using

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An Implementation of SDG Program for English, Mathematics Cooperative Study

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Implementation of Indoor Location-Aware System based on Probability Distribution of RSSI

Abstract This article employs Naive Bayesian Classifier to realize a system that can distinguish the sex of a name. Unlike foreign names, in Korean names, the pronoun referring to a person shows discordance with sex. With the characteristics of Korean names, however, the study distinguishes names frequently used for men and for women. And as it also includes names of which sex is rather ambiguous such as proper nouns, the accuracy of it is somewhat low. The result of the experiment conducted in this article indicates 84% accuracy for Korean men and 88% for Korean women; thus, the total accuracy equals 86%. Meanwhile, about foreign names, men show 80% accuracy, and women 84%, so the total accuracy equals 83%. Key Words : Naive Bayesian, Persons names, Classification, Gender * 종신회원, 을지대학교 의료IT마케팅학과 ** 준회원, 을지대학교 의료IT마케팅학과 *** 정회원, 을지대학교 의료IT마케팅학과접수일자 :2013년 11월 8일, 수정완료 : 2013년 12월 8일게재확정일자 : 2013년 12월 13일Received: 8 November, 2013 / Revised: 8 December, 2013 Accepted: 13 December, 2013