• Title/Summary/Keyword: Relation Classification

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Cost Ratios for Cost and ROC Curves (비용곡선과 ROC곡선에서의 비용비율)

  • Hong, Chong-Sun;Yoo, Hyun-Sang
    • Communications for Statistical Applications and Methods
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    • v.17 no.6
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    • pp.755-765
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    • 2010
  • For classification problems on mixture distribution, a threshold based on cost functions is optimal from the viewpoint of a minimum expected cost. Assuming that there is no cost information, we propose cost ratios in the expected cost corresponding to thresholds where the total accuracy and the true rate are maximized to explain the relation of these cost ratios minimizing the expected cost. Other cost ratios are also proposed by comparing the normalized expected costs when classification accuracy is maximized. The values of these cost ratios are located between two cost ratios for the expected costs based on classification accuracies, and converge to that of the minimum expected cost. This work suggests two cost ratios: one is minimized by the expected cost and the normalized expected cost, and the other in the expected cost and the normalized expected cost functions that are maximized classification accuracies. We discuss their compatibility based on the relation of these cost ratios.

Analysis of Relation of Class Separability According to Different Kind of Satellite Images (위성영상의 종류에 따른 분리도 특성의 상관관계 분석)

  • Hong, Soon-Heon
    • 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 $1,600{\sim}2,000$.

Pattern Classification Based on the Selective Perception Ability of Human Beings (인간 시각의 선택적 지각 능력에 기반한 패턴 분류)

  • Kim Do-Hyeon;Kim Kwang-Baek;Cho Jae-Hyun;Cha Eui-Young
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.10 no.2
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    • pp.398-405
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    • 2006
  • We propose a pattern classification model using a selective perception ability of human beings. Generally, human beings recognize an object by putting a selective concentration on it in the region of interest. Much better classification and recognition could be possible by adapting this phenomenon in pattern classification. First, the pattern classification model creates some reference cluster patterns in a usual way. Then it generates an SPM(Selective Perception Map) that reflects the mutual relation of the reference cluster patterns. In the recognition phase, the model applies the SPM as a weight for calculating the distance between an input pattern and the reference patterns. Our experiments show that the proposed classifier with the SPM acquired the better results than other approaches in pattern classification.

Basic properties survey report on the rock classification (암반 등급분류를 위한 기초 물성조사 보고서)

  • Huh, Ginn
    • Journal of the Korean Professional Engineers Association
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    • v.24 no.3
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    • pp.43-50
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    • 1991
  • On the ground foundation works for Bldg site, Rock classification test can be obtained as follows due to the International Society for Rock Mechanics. 1. In-take test ; Compression strength, Point load test. 2. In-situ test : Schmidt hammer test. Burden test finaly the convinient co-relation table between strength and S.H. test were carried out for site-engineer. This project is one of contineous works regarding to Burden test from Jack leg drill( ø 36mm) to Crawler drill( ø 75mm) use.

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Basic properties survey report on the rock classification (암반 분류 기초 물성조사)

  • Huh Ginn
    • Explosives and Blasting
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    • v.9 no.3
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    • pp.10-16
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    • 1991
  • On the ground foundation works for Bldg site, Rock classification test can be obtained as follows due to the International Society for Rock Mechanics. 1. In-situ test : Compressive strength, Point load test. 2. In-situ test Schmidt hammer test. Burden test finaly the convinient co-relation table between strength and 5. H, test were carried out for site-engineer, This project is one of contineous works regarding to Burden test from Jack leg drill($\phi{\;}75mm$) use.

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Flood Hazard Map in Kumagaya City

  • Tanaka, Seiichiro;Ogawa, Susumu
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.763-765
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    • 2003
  • We made a hazard map using GIS and remote sensing for he greatest inundation damage that happened for the 20th century. We calculated the land cover classification using Landsat from 1983 to 2000. We calculated it from a damage report and an aerial photo for a flood. We considered relation of both land cover classification and the damage. We expected the inundation damage in the future and made a hazard map.

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Study on Rock classification of Rock Socketed Drilled Shaft (현장타설말뚝의 암반 근입부 암판정 사례연구)

  • Park, Woan-Suh;Yoo, Jai-Hyun;Lee, Woo-Cheol;Joo, Yong-Sun
    • Proceedings of the Korean Geotechical Society Conference
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    • 2010.09a
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    • pp.658-663
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    • 2010
  • Recently the most of deep foundation were socketed into weathered rock or soft rock to carry large foundation loads. The end bearing behavior of piles socketed in rock is generally dependent on the rock mass conditions with discontinuities and rock strength. Therefore, it is very important that the estimating rock classification with relation of TCR, RQD and unpredicted rock condition. In this study, the construction failure example of drilled shaft due to mistaking to estimate the rock classification on penetration were analyzed in site, so we hope to discuss problems of determining the rock socketed length of drilled shaft on construction.

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A Study on Children's Family Drawings by Attachment Classification (아동기 애착 유형에 따른 아동의 가족화 연구)

  • Jin, Mi Kyoung;Lee, Kyung Sook
    • Korean Journal of Child Studies
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    • v.28 no.4
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    • pp.187-196
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    • 2007
  • This study evaluated attachment representation of school aged children, its relation to classification of family drawings, and their association with children's perceptions about families. The attachment representation of 43 children 6-9 years of age was evaluated by the Manchester Attachment Story Task (Green, Stanley, & Goldwyn, 2003) children's family drawings were classified by Fury's Family Drawing Scales (1996). Results showed that 12 children (28%) were avoidant, 23 (54%) secure, 4 (9%) resistant, and 4 (9%) were disorganized. Classification of childhood attachment representation showed a high concordance rate (86%) with family drawings. Securely Attached children showed positive perceptions such as family pride/happiness and vitality/creativity while Insecure children showed negative perceptions like emotional distance, tension and bizarreness.

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A PROSET STRUCTURE INDUCED FROM HOMOTOPY CLASSES OF MAPS AND A CLASSIFICATION OF FIBRATIONS

  • Yamaguchi, Toshihiro;Yokura, Shoji
    • Communications of the Korean Mathematical Society
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    • v.34 no.3
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    • pp.991-1004
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    • 2019
  • Firstly we consider preorders (not necessarily partial orders) on a canonical quotient of the set of the homotopy classes of continuous maps between two spaces induced by a certain equivalence relation ${\sim}_{{\varepsilon}R}$. Secondly we apply it to a classification of orientable fibrations over Y with fibre X. In the classification theorem of J. Stasheff [22] and G. Allaud [3], they use the set $[Y,\;Baut_1X]$ of homotopy classes of continuous maps from Y to $Baut_1X$, which is the classifying space for fibrations with fibre X due to A. Dold and R. Lashof [11]. In this paper we give a classification of fibrations using a preordered set (abbr., proset) structure induced by $[Y,\;Baut_1X]_{{\varepsilon}R}:=[Y,\;Baut_1X]/{\sim}_{{\varepsilon}R}$.