• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2002.11a
• /
• pp.399-408
• /
• 2002

다중 에이전트 학습이란 다중 에이전트 환경에서 에이전트간의 조정을 위한 행동전략을 학습하는 것을 말한다. 본 논문에서는 에이전트간의 통신이 불가능한 다중 에이전트 환경에서 각 에이전트들이 서로 독립적으로 대표적인 강화학습법인 Q학습을 전개함으로써 서로 효과적으로 협조할 수 있는 행동전략을 학습하려고 한다. 하지만 단일 에이전트 경우에 비해 보다 큰 상태-행동 공간을 갖는 다중 에이전트환경에서는 강화학습을 통해 효과적으로 최적의 행동 전략에 도달하기 어렵다는 문제점이 있다. 이 문제에 대한 기존의 접근방법은 크게 모듈화 방법과 일반화 방법이 제안되었으나 모두 나름의 제한을 가지고 있다. 본 논문에서는 대표적인 다중 에이전트 학습 문제의 예로서 먹이와 사냥꾼 문제(Prey and Hunters Problem)를 소개하고 이 문제영역을 통해 이와 같은 강화학습의 문제점을 살펴보고, 해결책으로 신경망 SOM을 이용한 일반화 방법인 QSOM 학습법을 제안한다. 이 방법은 기존의 일반화 방법과는 달리 군집화 기능을 제공하는 신경망 SOM을 이용함으로써 명확한 다수의 훈련 예가 없어도 효과적으로 이전에 경험하지 못했던 상태-행동들에 대한 Q값을 예측하고 이용할 수 있다는 장점이 있다. 또한 본 논문에서는 실험을 통해 QSOM 학습법의 일반화 효과와 성능을 평가하였다.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.1
• /
• pp.87-98
• /
• 2010

This research deals the method to assess the validity and reliability of students' evaluation for lectures. Most papers for student's evaluation have focused the procedures for controlling the external effects, but this paper is trying to answer for "How reliable is the student rating?" An empirical study shows that the evaluations in Jeonju University have the fair validity and reliability. The generalizability theory is suggested to obtain the more comprehensive results rather than Cronbach's alpha to examine internal consistency.

• 한국지구과학회:학술대회논문집
• /
• 2004.02a
• /
• pp.18-19
• /
• 2004

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.2
• /
• pp.235-245
• /
• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.14 no.1
• /
• pp.27-38
• /
• 1996

Non-scale digital map have important role in the field of GIS and other application area which using geographical data in recently against conventional map restricted by scale and information. The main objective of this study is to develope the automated map production system for small scale map in conjuction with generalization techniques in a digital environment. We will intend to develope algorithms and programs for each generalization operators based on specific terrain feature with vector data. This study will be performed aspects related to an data model development of generalization process, focussing on priority for processing sequency with maintaining vector topology, and error analysis for generalized digital data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.13 no.1
• /
• pp.77-83
• /
• 1995

This paper is concentrated on map generalization in digital environment for automated multi-scale map pro-duction using conventional hardcopy maps. Line generalization is urgently required process to prepare small scale digital map database when large scale map databases are available. This paper outlines a new approach to the line generalization when preparing small scale map on the basis of existing large scale distal map. Line generalizations are conducted based on zero-crossing algorithm using six sheets of 115,000 scale YEOSU area which produced by National Geographic Institute. The results are compared to Douglas-Peucker algorithm and manual method. The study gives full details of the data reduction rates and alternatives based on the proposed algorithm.

• The Journal of Korean Association of Computer Education
• /
• v.22 no.5
• /
• pp.67-77
• /
• 2019

The purpose of this study is to develop and validate a general-purpose evaluation tool and to analyze their applicability in problem solving programming education for college students of teacher training college. For this purpose, we have redefined the area of computational thinking and detail elements from the viewpoint of problem solving programming, and developed general-purpose computational thinking scale to evaluate them. The reliability and validity were analyzed by applying the evaluation tool developed for the actual college students of teacher training college. Through this study, it was confirmed that the a general-purpose evaluation tool developed in this study can be used as a tool to computational thinking assessment and can be generalized.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2004.11a
• /
• pp.493-496
• /
• 2004

본 연구에서는 신경망 학습의 중요한 평가 척도로써 고려될 수 있는 일반화 성능과 학습속도를 개선시키기 위한 방안으로써 초기 가중값과 학습률과 같은 주요 인자들을 이용한 신경망 학습 영향을 살펴본다. 특히 초기 가중값과 학습률을 고정시킨 후 새롭게 조정된 계수들을 점차적으로 변화시키는 새로운 인자 결합방법을 이용하여 신경망 학습량과 학습속도를 비교해 보고 계수조정을 통한 개선된 학습 영향을 살펴본다. 그리고 단순한 예제를 이용한 실증분석을 통하여 신경망 모형의 일반화 성능과 학습 속도 개선을 위한 각 인자들의 개별 효과와 결합 효과를 살펴보고 그 개선 방안을 제시한다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2024.01a
• /
• pp.73-75
• /
• 2024

본 논문에서는 밀 수확량을 증가시키기 위한 일반화된 검출 모델을 제안한다. 일반화 성능을 높이기 위해 CutMix 알고리즘으로 데이터를 증식시켰고, 라벨링 되지 않은 데이터를 최대한 활용하기 위해 Fast R-CNN 기반 Pseudo labeling을 사용하였다. 학습의 정확성과 효율성을 높이기 위해 사전에 훈련된 EfficientDet 모델로 학습하였으며, OOF를 이용하여 검증하였다. 최신 객체 검출 모델과 IoU(Intersection over Union)를 이용한 성능 평가 결과, 제안된 모델이 가장 높은 성능을 보이는 것을 확인하였다.

• Journal of Korea Society of Industrial Information Systems
• /
• v.10 no.1
• /
• pp.28-37
• /
• 2005

In our previous works, we suggested a method that extracts representative keywords from a few positive documents and assigns weights to them. To show the usefulness of the method, in this paper, we evaluate the performance of a famous classification algorithm called GIS(Generalized Instance Set) when it is combined with our method. In GIS algorithm, generalized instances are built from learning documents by a generalization function and then the K-NN algorithm is applied to them. Here, our method is used as a generalization function. For comparative works, Rocchio and Widrow-Hoff algorithms are also used as a generalization function. Experimental results show that our method is better than the others for the case that only positive documents are considered, but not when negative documents are considered together.