• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.543-549
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• 2008

Inspired by using a robust loss function in the support vector machine regression to control training error and the idea of robust template matching with M-estimator, Chen (2004) applies M-estimator techniques to gaussian radial basis functions and form a new class of robust kernels for the support vector machines. We are specially interested in the shape of the Huber's M-estimator in this context and propose a way to find the shape parameter of the Huber's M-estimating function. For simplicity, only the two-class classification problem is considered.

• Asia pacific journal of information systems
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• v.15 no.2
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• pp.51-63
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• 2005

There has been substantial research into the bankruptcy prediction. Many researchers used the statistical method in the problem until the early 1980s. Since the late 1980s, Artificial Intelligence(AI) has been employed in bankruptcy prediction. And many studies have shown that artificial neural network(ANN) achieved better performance than traditional statistical methods. However, despite ANN's superior performance, it has some problems such as overfitting and poor explanatory power. To overcome these limitations, this paper suggests a relatively new machine learning technique, support vector machine(SVM), to bankruptcy prediction. SVM is simple enough to be analyzed mathematically, and leads to high performances in practical applications. The objective of this paper is to examine the feasibility of SVM in bankruptcy prediction by comparing it with ANN, logistic regression, and multivariate discriminant analysis. The experimental results show that SVM provides a promising alternative to bankruptcy prediction.

• Communications for Statistical Applications and Methods
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• v.15 no.3
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• pp.441-450
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• 2008

In this paper we propose a new method for solving multiclass problem with least squares support vector machine(LS-SVM) regression. This method implements one-against-all scheme which is as accurate as any other approach. We also propose cross validation(CV) method to select effectively the optimal values of hyper-parameters which affect the performance of the proposed multiclass method. Experimental results are then presented which indicate the performance of the proposed multiclass method.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.483-488
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• 2010

An online monitoring based upon sensor system is essential to assure both efficient operation and safety in the power plant. Of great importance is modeling for auto-association (AA) in online monitoring technique. The objective of auto-associative models lies in predicting true values of plant operation parameters from sensor signals transmitted. This paper presents two AA models using Support Vector Regression (SVR) and Partial Least Square Regression (PLSR). The presented models are useful, in particular, when there are many parameters to monitor in the power plant. Illustrative examples are given by using a real-world plant dataset. AA performances of SVR and PLSR are finally summarized in terms of accuracy and sensitivity. According to our results, SVR shows much higher accuracy and, however, its sensitivity is relatively degraded.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.65-75
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• 2002

The support vector machine (SVM), first developed by Vapnik and his group at AT &T Bell Laboratories, is being used as a new technique for regression and classification problems. In this paper we present an approach to estimating approximate prediction intervals for SVM regression based on posterior predictive densities. Furthermore, the method is illustrated with a data example.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.149-152
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• 2004

Speed and torque control of PMSM(Permanent Magnet Synchronous Motor) are usually achieved by using position and speed sensors which require additional mounting space, reduce the reliability in harsh environments and increase the cost of a motor. Therefore, many studies have been performed for the elimination of speed and position sensors. In this paper, a novel speed sensorless control of a permanent magnet synchronous motor based on SVMR(Support Vector Machine Regression) is presented. The SVM regression method is an algorithm that estimates an unknown mapping between a system's input and outputs, from the available data or training data. Two well-known different voltage model is necessary to estimate the speed of a PMSM. The validity and the usefulness of proposed algorithm are thoroughly verified through numerical simulation.

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.155-163
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• 2005

This paper applies Support Vector Regression (SVR) to estimate and forecast nonlinear autoregressive integrated (ARI) model of the daily exchange rates of four currencies (Swiss Francs, Indian Rupees, South Korean Won and Philippines Pesos) against U.S. dollar. The forecasting abilities of SVR are compared with linear ARI model which is estimated by OLS. Sensitivity of SVR results are also examined to kernel type and other free parameters. Empirical findings are in favor of SVR. SVR method forecasts exchange rate level better than linear ARI model and also has superior ability in forecasting the exchange rates direction in short test phase but has similar performance with OLS when forecasting the turning points in long test phase.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.401-408
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• 2013

Soil moisture is a very important variable in various area of hydrological processes. We predict the soil moisture using a support vector regression. The model is trained and tested using the soil moisture data observed in five sites in the Yongdam dam basin. With respect to soil moisture data of of four sites-Jucheon, Bugui, Sangieon and Ahncheon which are used to train the model, the correlation coefficient between the esimtates and the observed values is about 0.976. As the result of the application to Cheoncheon2 for validating the model, the correlation coefficient between the estimates and the observed values of soil moisture is about 0.835. We compare those results with those of artificial neural network models.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.589-596
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• 2011

The conditional autoregressive value at risk (CAViaR) model is useful for risk management, which does not require the assumption that the conditional distribution does not vary over time but the volatility does. But it does not provide volatility forecasts, which are needed for several important applications such as option pricing and portfolio management. For a variety of probability distributions, it is known that there is a constant relationship between the standard deviation and the distance between symmetric quantiles in the tails of the distribution. This inspires us to use a support vector quantile regression (SVQR) for volatility forecasts with the distance between CAViaR forecasts of symmetric quantiles. Simulated example and real example are provided to indicate the usefulness of proposed forecasting method for volatility.

• Proceedings of the KIEE Conference
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• 2004.04a
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• pp.106-108
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• 2004

서보 시스템의 전체 제어 성능은 기계적 상수의 변화와 부하 토크의 영향을 크게 받는다. 그러므로 서보 시스템의 성능을 향상시키기 위해서는 기계적 상수와 부하 토크를 정확히 알 필요가 있다. 본 논문에서는 Support Vector Regression (SVR)을 이용한 기계적 상수와 부하 토크의 추정 알고리즘을 제안한다. 여기서 제안된 추정 알고리즘인 SVR은 통계적인 학습 이론을 기반으로 한 새로운 추정 알고리즘으로 적은 샘플, 비선형, 국부해의 문제를 극복하고 강력한 성능을 발휘한다. 실험 결과는 제안된 SVR 알고리즘이 기계적 상수와 부하토크를 비교적 정확하게 추정하고 있음을 보여준다.