• Journal of the Korean Data and Information Science Society
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• 제14권1호
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• pp.103-111
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• 2003

Kernel machines are used widely in real-world regression tasks. Kernel ridge regressions(KRR) and support vector machines(SVM) are typical kernel machines. Here, we focus on two types of KRR. One is inductive KRR. The other is transductive KRR. In this paper, we study how differently they work in the interpolation and extrapolation areas. Furthermore, we study prediction interval estimation method for KRR. This turns out to be a reliable and practical measure of prediction interval and is essential in real-world tasks.

• Journal of applied mathematics & informatics
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• 제8권3호
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• pp.1009-1019
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• 2001

We introduce the concepts of TL-finite state machine, TL-kernel and TL-subfinite state machines, TL-kernel and TL-subfinite state machine and obtain some results concerning them.

• Communications for Statistical Applications and Methods
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• 제23권1호
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• pp.71-83
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• 2016

In this paper we discuss a deconvolution density estimator obtained using the support vector machines (SVM) and Tikhonov's regularization method solving ill-posed problems in reproducing kernel Hilbert space (RKHS). A remarkable property of SVM is that the SVM leads to sparse solutions, but the support vector deconvolution density estimator does not preserve sparsity as well as we expected. Thus, in section 3, we propose another support vector deconvolution estimator (method II) which leads to a very sparse solution. The performance of the deconvolution density estimators based on the support vector method is compared with the classical kernel deconvolution density estimator for important cases of Gaussian and Laplacian measurement error by means of a simulation study. In the case of Gaussian error, the proposed support vector deconvolution estimator shows the same performance as the classical kernel deconvolution density estimator.

• 제어로봇시스템학회논문지
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• 제12권4호
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• pp.315-319
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• 2006

Support vector machines in biped humanoid robot are presented in this paper. The trajectory of the ZMP in biped walking robot poses an important criterion for the balance of the walking robots but complex dynamics involved make robot control difficult. We are establishing empirical relationships based on the dynamic stability of motion using SVMs. SVMs and kernel method have become very popular method for learning from examples. We applied SVM to model the practical humanoid robot. Three kinds of kernels are employed also and each result has been compared. As a result, SVM based on kernel method have been found to work well. Especially SVM with RBF kernel function provides the best results. The simulation results show that the generated ZMP from the SVM can be improve the stability of the biped walking robot and it can be effectively used to model and control practical biped walking robot.

• Journal of the Korean Data and Information Science Society
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• 제24권3호
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• pp.625-636
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• 2013

In this paper we study four kernel machines for estimating expected shortfall, which are constructed through combinations of support vector quantile regression (SVQR), restricted SVQR (RSVQR), least squares support vector machine (LS-SVM) and support vector expectile regression (SVER). These kernel machines have obvious advantages such that they achieve nonlinear model but they do not require the explicit form of nonlinear mapping function. Moreover they need no assumption about the underlying probability distribution of errors. Through numerical studies on two artificial an two real data sets we show their effectiveness on the estimation performance at various confidence levels.

• 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.

• 대한전자공학회논문지SP
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• 제48권2호
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• pp.141-148
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• 2011

Support vector machine (SVM)은 패턴인식 분야에 많이 사용되어지고 있다. 한 예로서 3GPP2 selectable mode vocoder (SMV)와 같은 규격화된 코덱에 쓰여 코덱의 음성/음악 분류 성능을 향상시킬 수 있다. 본 논문에서는 SVM을 개선시켜 음성/음악의 분류성능을 향상시키는 새로운 방법을 제안한다. SVM을 학습시킬 때 적용되는 기존의 기법들과는 달리 제안되는 기법은 SVM이 패턴분류를 행할 때 사용된다. 그렇기 때문에 기존의 기법들과 독립적으로 개발되고 사용될 수 있고, 따라서 패턴분류의 성능을 한층 더 향상시킬 수 있다. 이를 위해 먼저 radial basis function의 커널 width 파라미터가 SVM의 패턴분류에 미치는 영향을 분석해 보았다. 분석한 결과, 커널 width 파라미터를 가지고 SVM의 패턴분류 성향을 미세 조정할 수 있다는 것을 알았다. 또한 음성신호의 각 프레임 간의 상관관계 (correlation)을 확인하고 이를 커널 width 파라미터조절의 길잡이로 삼았다. 실험을 통해, 제안된 기법이 SVM의 성능을 향상시킬 수 있음을 증명하였다.

• East Asian mathematical journal
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• 제37권3호
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• pp.333-354
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• 2021

A support vector machine (SVM) is a state-of-the-art machine learning model rooted in structural risk minimization. SVM is underestimated with regards to its application to real world problems because of the difficulties associated with its use. We aim at showing that the performance of SVM highly depends on which kernel function to use. To achieve these, after providing a summary of support vector machines and kernel function, we constructed experiments with various benchmark datasets to compare the performance of various kernel functions. For evaluating the performance of SVM, the F1-score and its Standard Deviation with 10-cross validation was used. Furthermore, we used taylor diagrams to reveal the difference between kernels. Finally, we provided Python codes for all our experiments to enable re-implementation of the experiments.

• Computers and Concrete
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• 제5권5호
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• pp.461-473
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• 2008

The paper explores the potential of Support Vector Machines (SVM) approach in predicting 28-day compressive strength and slump flow of self-compacting concrete. Total of 80 data collected from the exiting literature were used in present work. To compare the performance of the technique, prediction was also done using a back propagation neural network model. For this data-set, RBF kernel worked well in comparison to polynomial kernel based support vector machines and provide a root mean square error of 4.688 (MPa) (correlation coefficient=0.942) for 28-day compressive strength prediction and a root mean square error of 7.825 cm (correlation coefficient=0.931) for slump flow. Results obtained for RMSE and correlation coefficient suggested a comparable performance by Support Vector Machine approach to neural network approach for both 28-day compressive strength and slump flow prediction.

• 한국지능시스템학회논문지
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• 제21권5호
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• pp.618-623
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• 2011

본 논문에서는 구조 정보를 활용하기 위한 결합 커널 기반의 의미 있는 웹 테이블과 장식 웹 테이블을 구분하는 새로운 방법을 제안한다. 본 논문에서 테이블의 구조 정보는 두 가지 형태의 구문 분석 트리로부터 추출된다. 컨텍스트 트리는 테이블 주변에 나타난 구조를 반영하고 있으며, 테이블 트리는 테이블 내의 구조를 담고 있다. 두 트리로 표현되는 테이블의 구조 정보를 효과적으로 다루기 위해 파스 트리 커널 기반의 결합 커널을 제안한다. 제안한 결합 커널을 적용한 support vector machines은 풍부한 구조 정보를 활용하여 의미 있는 테이블과 장식 테이블을 분류한다.