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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Journal of the Korean Data and Information Science Society
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Korean Data and Information Science Society
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Volume 12, Issue 2 - Oct 2001
Volume 12, Issue 1 - Apr 2001
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A Study on Automatic Learning of Weight Decay Neural Network
Hwang, Chang-Ha ; Na, Eun-Young ; Seok, Kyung-Ha ;
Journal of the Korean Data and Information Science Society, volume 12, issue 2, 2001, Pages 1~10
Neural networks we increasingly being seen as an addition to the statistics toolkit which should be considered alongside both classical and modern statistical methods. Neural networks are usually useful for classification and function estimation. In this paper we concentrate on function estimation using neural networks with weight decay factor The use of weight decay seems both to help the optimization process and to avoid overfitting. In this type of neural networks, the problem to decide the number of hidden nodes, weight decay parameter and iteration number of learning is very important. It is called the optimization of weight decay neural networks. In this paper we propose a automatic optimization based on genetic algorithms. Moreover, we compare the weight decay neural network automatically learned according to automatic optimization with ordinary neural network, projection pursuit regression and support vector machines.
A Classification Analysis using Bayesian Neural Network
Hwang, Jin-Soo ; Choi, Seong-Yong ; Jun, Hong-Suk ;
Journal of the Korean Data and Information Science Society, volume 12, issue 2, 2001, Pages 11~25
There are several algorithms for classification in modeling relations, patterns, and rules which exist in data. We learn to classify objects on the basis of instances presented to us, not by being given a set of classification rules. The Bayesian learning uses the probability distribution to express our knowledge about unknown parameters and update our knowledge by the law of probability as the evidence gathered from data. Also, the neural network models are designed for predicting an unknown category or quantity on the basis of known attributes by training. In this paper, we compare the misclassification error rates of Bayesian Neural Network method with those of other classification algorithms, CHAID, CART, and QUBST using several data sets.
Asymptotic Distribution for Stopping Time in Estimating a Population Size
Choi, Ki-Heon ;
Journal of the Korean Data and Information Science Society, volume 12, issue 2, 2001, Pages 27~33
Suppose that there is a population of hidden objects of which the total number N is unknown. From such data, we derive an asymptotic distribution for stopping time.
Percentile Envelope and Its Characteristic of Error Distribution for Supernormality
Lee, Jea-Young ; Rhee, Seong-Won ;
Journal of the Korean Data and Information Science Society, volume 12, issue 2, 2001, Pages 35~45
We introduce a new percentile envelope for diagnosing supernormality in regression analysis. Furthermore, we compare this percentile envelope, which is much simpler and easier, with Atkinson's and Flack and Flores' envelopes. Using percentile envelope, we investigate characteristics of normal probability plots with envelope for error distributions when supernormality is occurred. We give cautions that test result for normality assumption of errors can be reached the wrong conclusion by supernormality.
A Comparative Study of Small Area Estimation Methods
Park, Jong-Tae ; Lee, Sang-Eun ;
Journal of the Korean Data and Information Science Society, volume 12, issue 2, 2001, Pages 47~55
Usually estimating the means is used for statistical inference. However depending the purpose of survey, sometimes totals will give the better and more meaningful in statistical inference than the means. Here in this study, we dealt with the unemployment population of small areas with using 4 different small area estimation methods: Direct, Synthetic, Composite, Bayes estimation. For all the estimates considered in this study, the average of absolute bias and men square error were obtained in the Monte Carlo Study which was simulated using data from 1998 Economic Active Population Survey in Korea.
Simultaneous Optimization of Multiple Responses to the Combined Array
Kwon, Yong-Man ;
Journal of the Korean Data and Information Science Society, volume 12, issue 2, 2001, Pages 57~64
In the Taguchi parameter design, the product-array approach using orthogonal arrays is mainly used. However, it often requires an excessive number of experiments. An alternative approach, which is called the combined-array approach, was suggested by Welch et al (1990) and studied by Vining and Myers (1990) and others. In these studies, only single respouse variable was considered. We propose how to simultaneously optimize multiple responses when there are correlations among responses.
The MLE and the UMVUE of the Right-Tail Probability in a Levy Distribution
Woo, Jung-Soo ; Lee, Hwa-Jung ;
Journal of the Korean Data and Information Science Society, volume 12, issue 2, 2001, Pages 65~69
MLE and UMVUE of the right-tail probability in a Levy distribution will be considered, and hence the UMVUE is more efficient in a sense of simulated MSE than the MLE of the right-tail probability.
Tests for Equality of Two Distributions with Life-Table Model
Kang, Shin-Soo ;
Journal of the Korean Data and Information Science Society, volume 12, issue 2, 2001, Pages 71~82
There are several ways to test the equality of two survival distributions under a variety of situations. Tests for equality of two distributions with life-table model for univariate independent response times are reviewed and introduced. It is developed that the methodology to test it for correlated response times where treatments are applied to different independent sets of cohorts. Data, which can be separated into two independent sets, from an angioplasty study where more than one procedure is performed on some patients are used to illustrate this methodology.
A Kernel Approach to Discriminant Analysis for Binary Classification
Shin, Yang-Kyu ;
Journal of the Korean Data and Information Science Society, volume 12, issue 2, 2001, Pages 83~93
We investigate a kernel approach to discriminant analysis for binary classification as a machine learning point of view. Our view of the kernel approach follows support vector method which is one of the most promising techniques in the area of machine learning. As usual discriminant analysis, the kernel method can discriminate an object most likely belongs to. Moreover, it has some advantage over discriminant analysis such as data compression and computing time.
서포터벡터학습의 효율적 알고리즘
Seok, Gyeong-Ha ;
Journal of the Korean Data and Information Science Society, volume 12, issue 2, 2001, Pages 95~102
A Study on Forecasting of Overseas Tour - Gravity Model and Regression Model
Choi, Kyung-Ho ; Kim, Jae-Hoon ;
Journal of the Korean Data and Information Science Society, volume 12, issue 2, 2001, Pages 103~111
Now a day, overseas tour which is due to economic development grows very much. In this situation, a forecast of overseas tour is required to establish tourism policy for tourism marketing. In this paper, we compare regression model and gravity model for a forecast of overseas tour. Using gravity model, this study also suggests an attraction which is suitable to our situation, and suggested attraction is compared and analyzed with another.
Evaluations of predicted models fitted for data mining - comparisons of classification accuracy and training time for 4 algorithms
Lee, Sang-Bock ;
Journal of the Korean Data and Information Science Society, volume 12, issue 2, 2001, Pages 113~124
CHAID, logistic regression, bagging trees, and bagging trees are compared on SAS artificial data set as HMEQ in terms of classification accuracy and training time. In error rates, bagging trees is at the top, although its run time is slower than those of others. The run time of logistic regression is best among given models, but there is no uniformly efficient model satisfied in both criteria.
Kernel Estimation of Hazard Ratio Based on Censored Data
Choi, Myong-Hui ; Lee, In-Suk ; Song, Jae-Kee ;
Journal of the Korean Data and Information Science Society, volume 12, issue 2, 2001, Pages 125~143
We, in this paper, propose a kernel estimator of hazard ratio with censored survival data. The uniform consistency and asymptotic normality of the proposed estimator are proved by using counting process approach. In order to assess the performance of the proposed estimator, we compare the kernel estimator with Cox estimator and the generalized rank estimators of hazard ratio in terms of MSE by Monte Carlo simulation. Two examples are illustrated for our results.