• 한국지능시스템학회논문지
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• 제13권4호
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• pp.486-490
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• 2003

Software qualify models can predict the risk of faults in the software early enough for cost-effective prevention of problems. This paper introduces a least squares support vector machine (LS-SVM) as a fuzzy regression method for predicting fault ranges in the software under development. This LS-SVM deals with the fuzzy data with crisp inputs and fuzzy output. Predicting the exact number of bugs in software is often not necessary. This LS-SVM can predict the interval that the number of faults of the program at each session falls into with a certain possibility. A case study on software reliability problem is used to illustrate the usefulness of this LS -SVM.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.507-513
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• 2004

Least-squares support vector machine (LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. In this paper, we propose LS-SVM approach to evaluating fuzzy regression model with multiple crisp inputs and a Gaussian fuzzy output. The proposed algorithm here is model-free method in the sense that we do not need assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.

• Journal of the Korean Data and Information Science Society
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• 제17권2호
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• pp.467-474
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• 2006

In this paper we present a weighted support vector machine(SVM) and a weighted least squares support vector machine(LS-SVM) for the prediction in the heteroscedastic regression model. By adding weights to standard SVM and LS-SVM the better fitting ability can be achieved when errors are heteroscedastic. In the numerical studies, we illustrate the prediction performance of the proposed procedure by comparing with the procedure which combines standard SVM and LS-SVM and wild bootstrap for the prediction.

• Journal of the Korean Data and Information Science Society
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• 제27권3호
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• pp.827-833
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• 2016

In this paper, we propose a deep least squares support vector machine (LS-SVM) for regression problems, which consists of the input layer and the hidden layer. In the hidden layer, LS-SVMs are trained with the original input variables and the perturbed responses. For the final output, the main LS-SVM is trained with the outputs from LS-SVMs of the hidden layer as input variables and the original responses. In contrast to the multilayer neural network (MNN), LS-SVMs in the deep LS-SVM are trained to minimize the penalized objective function. Thus, the learning dynamics of the deep LS-SVM are entirely different from MNN in which all weights and biases are trained to minimize one final error function. When compared to MNN approaches, the deep LS-SVM does not make use of any combination weights, but trains all LS-SVMs in the architecture. Experimental results from real datasets illustrate that the deep LS-SVM significantly outperforms state of the art machine learning methods on regression problems.

• Journal of the Korean Data and Information Science Society
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• 제21권5호
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• pp.959-965
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• 2010

For multiclassification, it is often the case that some variables are not important while some variables are more important than others. We propose a novel algorithm for selecting such relevant variables for multiclassification. This algorithm is base on multiclass least squares support vector machine (LS-SVM), which uses results of multiclass LS-SVM using one-vs-all method. Experimental results are then presented which indicate the performance of the proposed method.

• Journal of the Korean Data and Information Science Society
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• 제23권4호
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• pp.739-748
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• 2012

최소제곱 서포트벡터기계 (least squares support vector machine)는 분류 및 비선형 회귀분석에서 유용하게 사용되고 있는 통계적 기법이다. 본 논문에서는 각 집단별로 생존자료가 관측된 경우 적용할 수 있는 LS-SVM을 제안한다. 제안된 모형은 임의우측 중도절단자료를 비선형 회귀모형에 적용할 수 있게 Kaplan- Meier의 중도절단분포의 추정값을 이용하여 구해진 가중값을 사용하고, 집단 간의 변동을 나타내기 위하여 임의효과항을 포함한다. 벌칙상수와 커널모수의 최적값을 구하기 위하여 일반화 교차타당성함수가 사용되고 모의실험에서는 임의효과항을 포함하지 않은 LS-SVM과 성능을 비교함으로써 제안된 방법의 우수성을 보이기로 한다.

• Journal of the Korean Data and Information Science Society
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• 제21권5호
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• pp.831-839
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• 2010

최소제곱 서포트벡터기계는 비선형회귀분석과 분류에 널리 쓰이는 커널기법이다. 본 논문에서는 금융시계열자료의 평균 및 변동성을 추정하기 위하여 평균의 추정 방법으로는 가중최소제곱 서포트벡터기계, 변동성의 추정 방법으로는 최소제곱 서포트벡터기계를 사용하는 비선형 평균 일반화 이분산 자기회귀모형을 제안한다. 제안된 모형은 선형 일반화 이분산 자기회귀모형 및 선형 평균 일반화 이분산 자기회귀모형보다 더 나은 추정 능력을 가진다는 것을 실제자료의 추정을 통하여 보였다.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.337-343
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• 2003

In this paper we present the prediction interval estimation method using bootstrap method for least squares support vector machine(LS-SVM) regression, which allows us to perform even nonlinear regression by constructing a linear regression function in a high dimensional feature space. The bootstrap method is applied to generate the bootstrap sample for estimation of the covariance of the regression parameters consisting of the optimal bias and Lagrange multipliers. Experimental results are then presented which indicate the performance of this algorithm.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 추계학술대회
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• pp.161-169
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• 2005

In this paper we propose a fuzzy c-regression model based on weighted least squares support vector machine(LS-SVM), which can be used to detect outliers in the switching regression model while preserving simultaneous yielding the estimates of outputs together with a fuzzy c-partitions of data. It can be applied to the nonlinear regression which does not have an explicit form of the regression function. We illustrate the new algorithm with examples which indicate how it can be used to detect outliers and fit the mixed data to the nonlinear regression models.

• Structural Monitoring and Maintenance
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• 제5권3호
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• pp.379-390
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• 2018

In this paper, a novel and effective damage diagnosis algorithm is proposed to detect and estimate damage using two stages least squares support vector machine (LS-SVM) and limited number of attached sensors on structures. In the first stage, LS-SVM1 is used to predict the unmeasured mode shapes data based on limited measured modal data and in the second stage, LS-SVM2 is used to predicting the damage location and severity using the complete modal data from the first-stage LS-SVM1. The presented methods are applied to a three story irregular frame and cantilever plate. To investigate the noise effects and modeling errors, two uncertainty levels have been considered. Moreover, the performance of the proposed methods has been verified through using experimental modal data of a mass-stiffness system. The obtained damage identification results show the suitable performance of the proposed damage identification method for structures in spite of different uncertainty levels.