• Journal of the Korean Data and Information Science Society
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• 제20권5호
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• pp.949-954
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• 2009

The autoregressive process is applied in this paper to kernel regression in order to infer nonlinear models for predicting responses. We propose a kernel method for the autoregressive data which estimates the mean function by kernel machines. We also present the model selection method which employs the cross validation techniques for choosing the hyper-parameters which affect the performance of kernel regression. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of mean function in the presence of autocorrelation between data.

• 대한음성학회지:말소리
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• 제66호
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• pp.105-116
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• 2008

In this research, we propose a speaker identification system using a kernel method which is expected to model the non-linearity of speech features well. We have been using principal component analysis (PCA) successfully, and extended to kernel PCA, which is used for many pattern recognition tasks such as face recognition. However, we cannot use kernel PCA for speaker identification directly because the storage required for the kernel matrix grows quadratically, and the computational cost grows linearly (computing eigenvector of $l{\times}l$ matrix) with the number of training vectors I. Therefore, we use greedy kernel PCA which can approximate kernel PCA with small representation error. In the experiments, we compare the accuracy of the greedy kernel PCA with the baseline Gaussian mixture models using MFCCs and PCA. As the results with limited enrollment data show, the greedy kernel PCA outperforms conventional methods.

• 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.

• Journal of the Korean Data and Information Science Society
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• 제12권2호
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• pp.83-93
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• 2001

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.

• 한국산학기술학회논문지
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• 제12권7호
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• pp.3251-3257
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• 2011

본 논문에서는 논리합성을 위한 공통식 추출 방법을 새롭게 제안한다. 제안하는 방법은 주어진 각 논리식들에서 커널/커널 쌍들과 코커널/커널 쌍을 추출한다. 커널/커널 쌍은 주어진 논리식을 부울 나눗셈에 의해 제수, 몫, 나머지로 논리식을 다시 표현하게 된다. 다음, 여러 논리식에서 산출된 제수, 몫들에서 공통식을 추출하는 커널 교집합에 의해 공통식을 구하는 방법을 제안한다. 실험 결과 기존의 공통식 산출 결과들과 비교했을 때 제안한 방법은 리터럴 개수를 줄일 수 있었다.

• Interaction and multiscale mechanics
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• 제2권3호
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• pp.295-319
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• 2009

In this work, we discuss a reproducing kernel collocation method (RKCM) for solving $2^{nd}$ order PDE based on strong formulation, where the reproducing kernel shape functions with compact support are used as approximation functions. The method based on strong form collocation avoids the domain integration, and leads to well-conditioned discrete system of equations. We investigate the convergence and the computational complexity for this proposed method. An important result obtained from the analysis is that the degree of basis in the reproducing kernel approximation has to be greater than one for the method to converge. Some numerical experiments are provided to validate the error analysis. The complexity of RKCM is also analyzed, and the complexity comparison with the weak formulation using reproducing kernel approximation is presented.

• Communications for Statistical Applications and Methods
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• 제19권4호
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• pp.517-526
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• 2012

In this paper a support vector method is proposed for use when the sample observations are contaminated by a normally distributed measurement error. The performance of deconvolution density estimators based on the support vector method is explored and compared with kernel density estimators by means of a simulation study. An interesting result was that for the estimation of kurtotic density, the support vector deconvolution estimator with a Gaussian kernel showed a better performance than the classical deconvolution kernel estimator.

• Communications for Statistical Applications and Methods
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• 제17권3호
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• pp.451-457
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• 2010

This paper considers the problem of nonparametric deconvolution density estimation when sample observa-tions are contaminated by double exponentially distributed errors. Three different deconvolution density estima-tors are introduced: a weighted kernel density estimator, a kernel density estimator based on the support vector regression method in a RKHS, and a classical kernel density estimator. The performance of these deconvolution density estimators is compared by means of a simulation study.

• 한국산업융합학회 논문집
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• 제3권1호
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• pp.17-27
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• 2000

A factorization is an extremely important part of multi-level logic synthesis. The number of literals in a factored form is a good estimate of the complexity of a logic function. and can be translated directly into the number of transistors required for implementation. Factored forms are described as either algebraic or Boolean, according to the trade-off between run-time and optimization. A Boolean factored form contains fewer number of literals than an algebraic factored form. In this paper, we present a new method for a Boolean factorization. The key idea is to build an extended co-kernel cube matrix using co-kernel/kernel pairs and kernel/kernel pairs together. The extended co-kernel cube matrix makes it possible to yield a Boolean factored form. We also propose a heuristic method for covering of the extended co-kernel cube matrix. Experimental results on various benchmark circuits show the improvements in literal counts over the algebraic factorization based on Brayton's co-kernel cube matrix.

• 한국산학기술학회논문지
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• 제22권5호
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• pp.561-566
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• 2021

오차분포 추정을 위한 커널 사이즈는 오차확률밀도 사이의 유클리드 거리를 최소화 알고리즘의 가중치 갱신에 적합한 커널 사이즈가 될 수 없다. 이 논문에서는 MED 알고리즘의 수렴 성능 향상을 위해 적응적으로 커널 사이즈를 갱신하는 방법을 제안하였다. 제안한 방식은 MED 학습 알고리즘의 가중치 갱신을 위해 커널 사이즈에 대한 오차분산의 평균변화율을 도입하여 MED의 오차에 대한 평균전력이 감소하는 방향으로 커널 사이즈를 조절하도록 하였다. 제안된 적응 커널 추정법을 무선통신 채널의 왜곡 보상에 적용하여 학습 성능을 실험하고 그 효능을 밝혔다. 오차분산에 비례한 작은 값을 가지는 기존의 오차분포 추정 위한 최적 커널 사이즈와 달리, 제안한 방법에 의한 커널 사이즈는 MED 가중치 수렴을 위한 적절한 커널 사이즈로 수렴함을 보였다. 실험 결과로부터 제안한 방법이 MED 알고리즘의 커널 사이즈 설정에 따른 민감성을 크게 해결한 방법이라고 볼 수 있다.