• Title/Summary/Keyword: Kernel function

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Multi-Radial Basis Function SVM Classifier: Design and Analysis

  • Wang, Zheng;Yang, Cheng;Oh, Sung-Kwun;Fu, Zunwei
    • Journal of Electrical Engineering and Technology
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    • 제13권6호
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    • pp.2511-2520
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    • 2018
  • In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.

On Estimating the Hazard Rate for Samples from Weighted Distributions

  • Ahmad, Ibrahim A.
    • International Journal of Reliability and Applications
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    • 제1권2호
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    • pp.133-143
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    • 2000
  • Data from weighted distributions appear, among other situations, when some of the data are missing or are damaged, a case that is important in reliability and life testing. The kernel method for hazard rate estimation is discussed for these data where the basic large sample properties are given. As a by product, the basic properties of the kernel estimate of the distribution function for data from weighted distribution are presented.

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Censored Kernel Ridge Regression

  • Shim, Joo-Yong
    • Journal of the Korean Data and Information Science Society
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    • 제16권4호
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    • pp.1045-1052
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    • 2005
  • This paper deals with the estimations of kernel ridge regression when the responses are subject to randomly right censoring. The weighted data are formed by redistributing the weights of the censored data to the uncensored data. Then kernel ridge regression can be taken up with the weighted data. The hyperparameters of model which affect the performance of the proposed procedure are selected by a generalized approximate cross validation(GACV) function. Experimental results are then presented which indicate the performance of the proposed procedure.

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Mixed Effects Kernel Binomial Regression

  • Hwang, Chang-Ha
    • Journal of the Korean Data and Information Science Society
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    • 제19권4호
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    • pp.1327-1334
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    • 2008
  • Mixed effect binomial regression models are widely used for analysis of correlated count data in which the response is the result of a series of one of two possible disjoint outcomes. In this paper, we consider kernel extensions with nonparametric fixed effects and parametric random effects. The estimation is through the penalized likelihood method based on kernel trick, and our focus is on the efficient computation and the effective hyperparameter selection. For the selection of hyperparameters, cross-validation techniques are employed. Examples illustrating usage and features of the proposed method are provided.

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A New Method for Identifying Higher Volterra Kernel Having the Same Time Coordinate for Nonlinear System

  • Nishiyama, Eiji;Harada, Hiroshi;Rong, Li;Kashiwagi, Hiroshi
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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    • pp.137-140
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    • 1999
  • A lot of researcher have proposed a method of kernel identifying nonlinear system by use of Wiener kernels[6-7] or Volterra kernel[5] and so on. In this research, the authors proposed a method of identifying Volterra kernels for nonlinear system by use of pseudorandom M-sequence in which a crosscorrelation function between input and output of a nonlinear system is taken[4]. we can be applied to an MISO nonlinear system or a system which depends on its input amplitude[2]. But, there exist many systems in which it is difficult to determine a Volterra kernel having the same time coordinate on the crosscorrelation function. In those cases, we have to estimate Volterra kernel by using its neighboring points[4]. In this paper, we propose a new method for not estimating but obtaining Volterra kernel having the same time coordinate using calculation between the neighboring points. Some numerical simulations show that this method is effective for obtaining higher order Volterra kernel of nonlinear control systems.

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On Practical Efficiency of Locally Parametric Nonparametric Density Estimation Based on Local Likelihood Function

  • Kang, Kee-Hoon;Han, Jung-Hoon
    • Communications for Statistical Applications and Methods
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    • 제10권2호
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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.

Elongated Radial Basis Function for Nonlinear Representation of Face Data

  • 김상기;유선진;이상윤
    • 한국통신학회논문지
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    • 제36권7C호
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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.

Asymptotic Approximation of Kernel-Type Estimators with Its Application

  • 장유선;김성래;김성균
    • 한국전산응용수학회:학술대회논문집
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    • 한국전산응용수학회 2003년도 KSCAM 학술발표회 프로그램 및 초록집
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    • pp.12.1-12
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    • 2003
  • Sufficient conditions are given under which a generalized class of kernel-type estimators allows asymptotic approximation On the modulus of continuity This generalized class includes sample distribution function, kernel-type estimator of density function, and an estimator that may apply to the censored case. In addition, an application is given to asymptotic normality of recursive density estimators of density function at an unknown point.

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ESTIMATES OF THE BERGMAN KERNEL FUNCTION ON PSEUDOCONVEX DOMAINS WITH COMPARABLE LEVI FORM

  • Cho, Sang-Hyun
    • 대한수학회지
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    • 제39권3호
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    • pp.425-437
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    • 2002
  • Let $\Omega$ be a smoothly bounded pseudoconvex domain in $C^{n}$ and let $z^{0}$ $\in$b$\Omega$ a point of finite type. We also assume that the Levi form of b$\Omega$ is comparable in a neighborhood of $z^{0}$ . Then we get precise estimates of the Bergman kernel function, $K_{\Omega}$(z, w), and its derivatives in a neighborhood of $z^{0}$ . .