• Journal of the Korean Statistical Society
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• 제22권2호
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• pp.361-368
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• 1993

In recent years, kernel type estimates are abundant. In this paper, we propose a bandwidth selection method for kernel regression of fixed design based on bootstrap procedure. Mathematical properties of proposed bootstrap-based bandwidth selection method are discussed. Performance of the proposed method for small sample case is compared with that of cross-validation method via a simulation study.

• Journal of the Korean Data and Information Science Society
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• 제18권3호
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• pp.767-772
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• 2007

A kernel machine is proposed as an estimating procedure for the linear and nonlinear Poisson regression, which is based on the penalized negative log-likelihood. The proposed kernel machine provides the estimate of the mean function of the response variable, where the canonical parameter is related to the input vector in a nonlinear form. The generalized cross validation(GCV) function of MSE-type is introduced to determine hyperparameters which affect the performance of the machine. Experimental results are then presented which indicate the performance of the proposed machine.

• ETRI Journal
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• 제15권2호
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• pp.35-51
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• 1993

This paper presents function approximation based on nonparametric estimation. As an estimation model of function approximation, a three layered network composed of input, hidden and output layers is considered. The input and output layers have linear activation units while the hidden layer has nonlinear activation units or kernel functions which have the characteristics of bounds and locality. Using this type of network, a many-to-one function is synthesized over the domain of the input space by a number of kernel functions. In this network, we have to estimate the necessary number of kernel functions as well as the parameters associated with kernel functions. For this purpose, a new method of parameter estimation in which linear learning rule is applied between hidden and output layers while nonlinear (piecewise-linear) learning rule is applied between input and hidden layers, is considered. The linear learning rule updates the output weights between hidden and output layers based on the Linear Minimization of Mean Square Error (LMMSE) sense in the space of kernel functions while the nonlinear learning rule updates the parameters of kernel functions based on the gradient of the actual output of network with respect to the parameters (especially, the shape) of kernel functions. This approach of parameter adaptation provides near optimal values of the parameters associated with kernel functions in the sense of minimizing mean square error. As a result, the suggested nonparametric estimation provides an efficient way of function approximation from the view point of the number of kernel functions as well as learning speed.

• 농업과학연구
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• 제4권2호
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• pp.215-228
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• 1977

양질 옥수수 육성을 위한 우량한 유전자원을 공급하기 위하여 전국적으로 수집한 총 948개의 재래종 옥수수 이삭에 대하여 주요 형태적 특성을 조사하였던바, 다음과 같은 몇가지 결과를 얻었다. 1) 이삭형태 : 한국 재래종 옥수수의 이삭형태는 IV형 II형 III형 I형의 순으로 구성되었으며, 옥수수의 주산지인 북동부 산악지대에서는 원통형 내지는 원통형에 가까운 형태의 이삭이 많았고, 옥수수가 소규모로 재배되고 있는 남부 평야지대에서는 원추형 내지는 원추형에 가까운 형태의 이삭이 많았다. 2) 입색 : 전국 10개 지역에서 수집된 모든 재래종 옥수수 이삭 가운데서 단색립을 가진 이삭이 54.4%, 가지 색갈의 입색이 혼합된 이삭이 39.0%이없고 세가지 이상의 입색을 가진 이삭이 6.6%이었다. 북부산간지대에서는 이삭의 색갈은 백색혹은 백색과 다른 색갈이 혼합된 것이 많았고, 그 밖의 다른 지역에서는 이삭이 색갈이 노란색 혹은 노란색과 다른 색갈이 혼합된 것이 많았다. 3) 마치성 : 수집된 재래종 옥수수 가운데 불과 4.3%만이 마치종 이었고 나머지는 경립종이었다. 마치종은 외국 옥수수 품종이 도입된 북부 지방에서 수집되었다. 4) 이삭열수 : 수집된 재래종 옥수수의 이삭열수는 12열 내지 16열이 대부분이었고, 수집된 지역에 따라 분류된 이삭열수 사이에는 큰 차이가 없었다. 여하튼, 이삭열수는 입의 크기에 밀접한 관계가 있는 것으로 나타났다. 예를 들면, 이삭열수가 24개나 되는 이삭의 입은 그 크기가 매우 작았다. 5) 입질 : 수집된 재래종 옥수수의 70.9%가 경질립이었고, 연질립은 26.0% 그리고 중간형이 3.1%이었다. 옥수수의 주산지인 A지역과 I지역에서 경질 옥수수가 더 많이 수집 되었다. 6) 폭열종과 찰옥수수 : 수집된 재래종 옥수수를 보면 폭열종 옥수수는 I지역(울릉도)를 제외하고는 고르게 분포되어 있었고, 찰옥수수는 북부 산간지방에서 많았다. 7) 이삭길이 : 수집된 재래종 옥수수의 평균 이삭길이는 13cm이었고, A지역과 I지역에서 수집된 옥수수의 길이가 다른 지방의 것보다 길었다. 8) 이삭직경 : 수집된 재래종 옥수수의 평균 이삭직경은 3.3cm이었고, A지역과 I지역에서 수집된 옥수수의 이삭직경이 다른 지방의 것보다 컸다. 9) 입길이, 입폭, 입깊이 : 수집된 재래종 옥수수의 평균 입길이, 입폭, 입깊이는 각각 0.82cm, 0.42cm 그리고 0.78cm 이었다. A 지역과 I 지역에서 수집된 옥수수의 입의 다른 지역에서 수집된 옥수수의 입보다 컷다. 10) 이삭 무게 : 수집된 재래종 옥수수의 평균 이삭무게는 평균 58.04gr 이었으며 A 지역과 I 지역에서 수집된 것이 현저히 무거웠다. 가장 무거운 이삭의 무게는 330gr 이었고 가장 가벼운 이삭의 무게는 5gr 이었다. 11) 이삭당 입중 및 100입중 : 수집된 재래종 옥수수의 이삭당 입중은 평균 47.07gr 이었고 100입중은 평균 15.07gr 이었다. 이삭당 입중 및 100입중은 A 지역과 I 지역에서 수집된 것이 더 무거웠다.

• Kyungpook Mathematical Journal
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• 제50권2호
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• pp.297-306
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• 2010

In this paper, the integral operator is used. We give a new Hilbert-type integral inequality, whose kernel is the homogeneous form with degree - $\lambda$ and with three pairs of conjugate exponents and the best constant factor and its reverse form are also derived. It is shown that the results of this paper represent an extension as well as some improvements of the earlier results.

• Kyungpook Mathematical Journal
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• 제50권2호
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• pp.281-288
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• 2010

By introducing some parameters and using the way of weight function and the technic of real analysis and complex analysis, a new Hilbert-type integral inequality with a best constant factor and a combination kernel involving two mean values is given, which is an extension of Hilbert's integral inequality. As applications, the equivalent form and the reverse forms are considered.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권4호
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• pp.267-274
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• 2011

Recently, actor-critic methods have drawn significant interests in the area of reinforcement learning, and several algorithms have been studied along the line of the actor-critic strategy. In this paper, we consider a new type of actor-critic algorithms employing the kernel methods, which have recently shown to be very effective tools in the various fields of machine learning, and have performed investigations on combining the actor-critic strategy together with kernel methods. More specifically, this paper studies actor-critic algorithms utilizing the kernel-based least-squares estimation and policy gradient, and in its critic's part, the study uses a sliding-window-based kernel least-squares method, which leads to a fast and efficient value-function-estimation in a nonparametric setting. The applicability of the considered algorithms is illustrated via a robot locomotion problem and a tunnel ventilation control problem.

• 음성과학
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• 제12권4호
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• pp.17-29
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• 2005

Time-frequency representation reveals some useful information about instantaneous frequency, instantaneous bandwidth and boundary of each AM-FM component of a speech signal. In many cases, the instantaneous frequency of each component is not constant. The variability of instantaneous frequency causes degradation of resolution in time-frequency representation. This paper presents a method of adaptively adjusting the transform kernel for preventing degradation of resolution due to time-varying instantaneous frequency. The transform kernel is the form of frequency modulated function. The modulation function in the transform kernel is determined by the estimate of instantaneous frequency which is approximated by first order polynomial at each time instance. Also, the window function is modulated by the estimated instantaneous. frequency for mitigation of fringing. effect. In the proposed method, not only the transform kernel but also the shape and the length of. the window function are adaptively adjusted by the instantaneous frequency of a speech signal.

• Water Engineering Research
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• 제2권1호
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• pp.1-10
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• 2001

The frequency analyses for the precipitation data in Korea were performed. We used daily maximum series, monthly maximum series, and annual series. For nonparametric frequency analyses, variable kernel estimators were used. Nonparametric methods do not require assumptions about the underlying populations from which the data are obtained. Therefore, they are better suited for multimodal distributions with the advantage of not requiring a distributional assumption. In order to compare their performance with parametric distributions, we considered several probability density functions. They are Gamma, Gumbel, Log-normal, Log-Pearson type III, Exponential, Generalized logistic, Generalized Pareto, and Wakeby distributions. The variable kernel estimates are comparable and are in the middle of the range of the parametric estimates. The variable kernel estimates show a very small probability in extrapolation beyond the largest observed data in the sample. However, the log-variable kernel estimates remedied these defects with the log-transformed data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권7호
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• pp.3076-3092
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• 2020

An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.