• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.681-687
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• 2001

In order to raise a class discrimination power by combining multiple classifiers under the Bayesian decision theory, the upper bound of a Bayes error rate bounded by the conditional entropy of a class variable and decision variables obtained from training data samples should be minimized. Wang and Wong proposed a tree dependence first-order approximation scheme of a high order probability distribution composed of the class and multiple feature pattern variables for minimizing the upper bound of the Bayes error rate. This paper presents an extended high order product approximation scheme dealing with higher order dependency more than the first-order tree dependence, based on the minimization of the upper bound of the Bayes error rate. Multiple recognizers for unconstrained handwritten numerals from CENPARMI were combined by the proposed approximation scheme using the Bayesian formalism, and the high recognition rates were obtained by them.

• The KIPS Transactions:PartD
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• v.13D no.2 s.105
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• pp.199-206
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• 2006

Sequential pattern mining is an important data mining task with broad applications. However, conventional methods may meet inherent difficulties in mining databases with long sequences and noise. They may generate a huge number of short and trivial patterns but fail to find interesting patterns shared by many sequences. In this paper, to overcome these problems, we propose the theme of approximate sequential pattern mining roughly defined as identifying patterns approximately shared by many sequences. The proposed method works in two steps: one is to cluster target sequences by their similarities and the other is to find consensus patterns that ire similar to the sequences in each cluster directly through multiple alignment. For this purpose, a novel structure called weighted sequence is presented to compress the alignment result, and the longest consensus pattern that represents each cluster is generated from its weighted sequence. Finally, the effectiveness of the proposed method is verified by a set of experiments.

• Journal of Digital Contents Society
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• v.3 no.2
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• pp.197-209
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• 2002

If we reduce the number of vertexes to decrease bit rate in polygon-based shape coding, the distortion of approximated contour increases rapidly. On the other hand, if we reduce the distortion, the number of vertexes increases rapidly and many bits are required to encode the vertexes. To improve this problem, in this paper we propose the approximation vertex search method. The encoder in the proposed method searches the type of distortion patterns that is the most similar to the shape which polygon edge and contour segment form and then encodes it. And then, the decoder mathematically finds the approximated vertexes from decoded distortion pattern information. Therefore, the proposed algorithm results in encoding many vertexes at a low bit rate and having the smoother shape than conventional method. As shown in computer simulation, the proposed method has less distortion than conventional method. It costs less bit rate by $10{\sim}20%$ than conventional algorithm in same distortion.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.13 no.4
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• pp.427-436
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• 2000

This study presents the nonlinear responses of a hinged-clamped beam under broadband random excitation. By using Galerkin's method the governing equation is reduced to a system or nonautonomous nonlinear ordinary differential equations. The Fokker-Planck equation is used to generate a general first-order differential equation in the joint moments of response coordinates. Gaussian and non-Gaussian closure schemes are used to close the infinite coupled moment equations. The closed equations are then solved for response statistics in terms of system and excitation parameters. The case of two mode interaction is considered in order to compare it with the case of three mode interaction. Monte Carlo simulation is used for numerical verification.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.316-321
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• 2003

Recently, support vector learning attracts great interests in the areas of pattern classification, function approximation, and abnormality detection. It is well-known that among the various support vector learning methods, the so-called no-versions are particularly useful in cases that we need to control the total number of support vectors. In this paper, we consider the problem of function approximation utilizing both predetermined basis functions and a no-version support vector learning called $\nu-SVR$. After reviewing $\varepsilon-SVR$, $\nu-SVR$, and a semi-parametric approach, this paper presents an extension of the conventional $\nu-SVR$ method toward the direction that can utilize Predetermined basis functions. Moreover, the applicability of the presented method is illustrated via an example.

• Korean Chemical Engineering Research
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• v.50 no.5
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• pp.830-836
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• 2012

Dead times appear often in describing process dynamics and raise some difficulties in simulating process dynamics or analyzing process control systems. To relieve these difficulties, it is needed to approximate the infinite dimensional dead time by the finite dimensional transfer function and, for this, the Pade approximation method is often used. For the accurate approximation of the dead time, high order Pade approximation is needed and the high order Pade approximation is not easy to memorize and is not stable numerically. We propose a method based on the continued fraction expansion that provides the same transfer functions. The method is excellent numerically as well as systematic to be memorized easily. It can be used conveniently for the process control lecture and computations.

• Journal for History of Mathematics
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• v.25 no.3
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• pp.1-14
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• 2012

Since JiuZhang SuanShu(九章算術), the basic field of the traditional mathemtics in Eastern Asia is the field of rational numbers and hence irrational solutions of equations should be replaced by rational approximations. Thus approximate solutions of equations became a very important subject in theory of equations. We first investigate the history of approximate solutions in Chinese sources and then compare them with those in Chosun mathematics. The theory of approximate solutions in Chosun has been established in SanHakWonBon(算學原本) written by Park Yul(1621 - 1668) and JuSeoGwanGyun(籌書管見, 1718) by Cho Tae Gu(趙泰耉, 1660-1723). We show that unlike the Chinese counterpart, Park and Cho were concerned with errors of approximate solutions and tried to find better approximate solutions.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.527-531
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• 2008

For statistical modeling, the model parameters are usually estimated by maximizing a probability measure, such as the likelihood or the posterior. In contrast, a variational Bayesian method treats the parameters of a model as probability distributions and computes optimal distributions for them rather than values. It has been shown that this approach effectively avoids the overfitting problem, which is common with other parameter optimization methods. This paper applies a variational Bayesian technique to surface fitting for height field data. Then, we propose point cloud denoising based on the basic surface fitting technique. Validation experiments and further tests with scan data verify the robustness of the proposed method.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.532-534
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• 2021

최근 데이터의 양이 급격히 증가함에 따라 빅데이터 환경에서 데이터 질의 처리 수행 시 연산 시간이 많이 소요되는 문제점이 발생한다. 이러한 처리 시간을 줄이기 위한 방법으로 근사질의 처리에 대한 연구의 필요성이 대두되고 있다. 근사 질의 처리 방법은 정확도가 다소 떨어지더라도 빠른 결과를 요구하는 응용 분야에서 매우 유용하게 쓰일 수 있다. 본 논문에서는 사용자가 원하는 결과 정확도와 적시성 등을 지원하기 위한 근사 질의 처리 언어 확장, 실행 계획생성 및 질의 최적화 기술을 제안하고, 설계 방향 및 특징 등에 대해서 설명한다.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.287-302
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• 1998

Saddlepoint approximation to the distribution function of sample mean(Daniels, 1987) is extended to the case of general statistic in this paper. The suggested approximation methods are applied to derive the approximations to the distributions of some statistics, including sample valiance and studentized mean. Some comparisons with other methods show that the suggested approximations are very accurate for moderate or small sample sizes. Even in extreme tail the accuracies are also maintained.