• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.607-617
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• 2003

This paper offers a practical comparison of efficiency between local likelihood approach and conventional kernel approach in density estimation. The local likelihood estimation procedure maximizes a kernel smoothed log-likelihood function with respect to a polynomial approximation of the log likelihood function. We use two types of data driven bandwidths for each method and compare the mean integrated squares for several densities. Numerical results reveal that local log-linear approach with simple plug-in bandwidth shows better performance comparing to the standard kernel approach in heavy tailed distribution. For normal mixture density cases, standard kernel estimator with the bandwidth in Sheather and Jones(1991) dominates the others in moderately large sample size.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.175-177
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• 2003

This paper addresses a problem of classifying human breast cancer into its subtypes. A main ingredient in our approach is kernel machines such as support vector machine (SVM). kernel principal component analysis (KPCA). and kernel partial least squares (KPLS). In the task of breast cancer classification, we employ both SVM and KPLS and compare their results. In addition to this classification. we also analyze the patterns of clinical outcomes in the feature space. In order to visualize the clinical outcomes in low-dimensional space, both KPCA and KPLS are used. It turns out that these methods are useful to identify correlations between clinical outcomes and the nonlinearly protected expression profiles in low-dimensional feature space.

• Journal of the Korean Data and Information Science Society
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• v.3 no.1
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• pp.17-24
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• 1992

The problem of estimating symmetric probability density with high kurtosis is considered. Such densities are often estimated poorly by a global bandwidth kernel estimation since good estimation of the peak of the distribution leads to unsatisfactory estimation of the tails and vice versa. In this paper, we propose a transformation technique before using a global bandwidth kernel estimator. Performance of density estimator based on proposed transformation is investigated through simulation study. It is observed that our method offers a substantial improvement for the densities with high kurtosis. However, its performance is a little worse than that of ordinary kernel estimator in the situation where the kurtosis is not high.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.7C
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• pp.428-434
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• 2011

Recently, subspace analysis has raised its performance to a higher level through the adoption of kernel-based nonlinearity. Especially, the radial basis function, based on its nonparametric nature, has shown promising results in face recognition. However, due to the endemic small sample size problem of face data, the conventional kernel-based feature extraction methods have difficulty in data representation. In this paper, we introduce a novel variant of the RBF kernel to alleviate this problem. By adopting the concept of the nearest feature line classifier, we show both effectiveness and generalizability of the proposed method, particularly regarding the small sample size issue.

• East Asian mathematical journal
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• v.26 no.1
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• pp.9-23
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• 2010

In this paper we generalize large-update primal-dual interior point methods for linear optimization problems in [2] to the $P_*(\kappa)$ linear complementarity problems based on a new kernel function which includes the kernel function in [2] as a special case. The kernel function is neither self-regular nor eligible. Furthermore, we improve the complexity result in [2] from $O(\sqrt[]{n}(\log\;n)^2\;\log\;\frac{n{\mu}o}{\epsilon})$ to $O\sqrt[]{n}(\log\;n)\log(\log\;n)\log\;\frac{m{\mu}o}{\epsilon}$.

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.329-339
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• 1993

Kernel regression is a nonparametric regression technique which requires only differentiability of the true function. If one wants to use the kernel regression technique to produce smooth estimates of a curve over a finite interval, one can realize that there exist distinct boundary problems that detract from the global performance of the estimator. This paper develops a kernel to handle boundary problem. In order to develop the boundary kernel, a generalized jacknife method by Gray and Schucany (1972) is adapted. Also, it will be shown that the boundary kernel has the same order of convergence rate as non-boundary.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.86-88
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• 2014

Naive Bayes (NB) assumption has some harmful effects in classification to the real world data. To relax this assumption, we now propose approach called Naive Bayes Mutual Information Attribute Weighting with Smooth Kernel Density Estimation (NBMIKDE) that combine the smooth kernel for attribute and attribute weighting method based on mutual information measure.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.599-603
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• 2002

We propose a new meshfree method to be called the fast moving least square reproducing kernel collocation method(FCM). This methodology is composed of the fast moving least square reproducing kernel(FMLSRK) approximation and the point collocation scheme. Using point collocation makes the meshfree method really come true. In this paper, FCM Is shown to be a good method at least to calculate the numerical solutions governed by second order elliptic partial differential equations with geometric singularity or geometric multi-scales. To treat such problems, we use the concept of variable dilation parameter.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.107-117
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• 2004

Change point testing problem is considered. Kernel density estimators are used for constructing proposed change point test statistics. The proposed method can be used to the hypothesis testing of not only parameter change but also distributional change. Bootstrap method is applied to get the sampling distribution of proposed test statistic. Small sample Monte Carlo Simulation were also conducted in order to show the performance of proposed method.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.190-192
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• 2008

This paper shows the formulation of fast moving least square reproducing kernel method (FMLSRKM) which is a kind of meshfree methods. FMLSRKM has some advantages compared to conventional numerical techniques such as finite element method. For simple analysis on a rectangular waveguide, point collocation scheme is introduced and applied.