• 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.

• 대한전기학회논문지:전력기술부문A
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• 제55권7호
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• pp.306-312
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• 2006

Support Vector Machine(SVM), of which the foundations have been developed by Vapnik (1995), is gaining popularity thanks to many attractive features and promising empirical performance. In this paper, we propose a new short-term load forecasting technique based on SVM. We discuss the input vector selection of SVM for load forecasting and analyze the prediction performance for various SVM parameters such as kernel function, cost coefficient C, and $\varepsilon$ (the width of 8 $\varepsilon-tube$). The computer simulation shows that the prediction performance of the proposed method is superior to that of the conventional neural networks.

• 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.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2004년도 추계학술대회
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• pp.179-188
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• 2004

Bankruptcy prediction is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. Recently, support vector machine (SVM) has been applied to the problem of bankruptcy prediction. The SVM-based method has been compared with other methods such as neural network, logistic regression and has shown good results. Genetic algorithm (GA) has been increasingly applied in conjunction with other AI techniques such as neural network, CBR. However, few studies have dealt with integration of GA and SVM, though there is a great potential for useful applications in this area. This study proposes the methods for improving SVM performance in two aspects: feature subset selection and parameter optimization. GA is used to optimize both feature subset and parameters of SVM simultaneously for bankruptcy prediction.

• 한국멀티미디어학회논문지
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• 제10권7호
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• pp.923-933
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• 2007

본 논문에서는 SVM(Support Vector Machine)과 LDA(Linear Discriminant Analysis)를 이용하여 사각형 형태 마커 검출 및 인식의 성능을 향상시키는 방법을 제안한다. 본 논문의 방법에서는 사각형 형태의 마커 검출을 위하여 입력 영상을 이진 영상으로 변환하고 객체들의 윤곽선을 추출한 다음에 윤곽선을 선분으로 근사화 한다. 근사화된 선분으로부터 기하학적 특징을 이용하여 사각형을 찾는다. 마커의 사각형 영역을 찾은 다음에는 워핑 기법과 확대/축소 변환을 이용하여 사각형 영상을 정사각형 형태로 정규화한다. 정사각형 형태로 정규화한 다음에는 주성분 분석을 적용하여 특징 벡터의 크기를 줄인 다음에 SVM을 이용하여 마커 영상인지 아닌지를 검사한다. 마커 영상으로 판별된 영상에 대하여 LDA를 적용하여 특징 벡터의 크기를 더 줄이고 표준 마커에 대한 특징 벡터와의 최소 거리법에 의해 마커의 종류를 인식한다. 인식 실험 결과 SVM을 사용함으로써 마커 검출의 오류를 줄일 수 있었고 LDA를 사용함으로써 특징 벡터의 크기는 줄어들고 인식률이 높아짐을 알 수 있었다.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2012년도 추계 학술논문 발표대회
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• pp.153-154
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• 2012

The accuracy of cost estimation at an early stage in school building project is one of the critical factors for successful completion. So many method and techniques have developed that can estimate construction cost using limited information available in the early stage. Among the techniques, Support Vector Machine(SVM) has received attention in various field due to its excellent capacity for self-learning and generalization performance. Therefore, the purpose of this study is to verify the applicability of cost prediction model based on SVM in school building project at the early stage. Data used in this study are 139 school building cost constructed from 2004 to 2007 in Gyeonggi-Do. And prediction error rate of 7.48% in support vector machine is obtained. So the results showed applicability of using SVM model for predicting construction cost of school building projects.

• 한국음향학회지
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• 제28권5호
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• pp.471-476
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• 2009

본 논문에서는 변별적 가중치 학습 (discriminative weight training) 기반의 최적화된 가중치를 가지는 입력벡터를 구성하여 support vector machine (SVM)을 이용한 기존의 3GPP2 selectable mode vocoder (SMV)코덱의 음성/음악 분류 성능을 향상 시키는 방법을 제안한다. 구체적으로, 최소 분류 오차 minimum classification error (MCE) 방법을 도입하여, 최적화된 가중치를 각각의 특징벡터별로 부가한 SVM을 적용하여 기존의 가중치를 고려하지 않은 SVM 기반의 알고리즘과 비교하였으며, 우수한 음성/음악 분류 성능을 보였다.

• 디지털콘텐츠학회 논문지
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• 제13권2호
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• pp.189-195
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• 2012

본 논문에서는 휴먼 인지를 위한, 근적외선 얼굴 영상에서의 얼굴 검출 방법이 제안된다. 에지의 강도와 방향에 기반한 에지 히스토그램이 근적외선 영상으로부터 얼굴을 검출하기 위해 사용되었다. 조명변화에 강인하기 때문에, 제안된 에지 히스토그램은 얼굴을 효과적으로 표현하고 구별한다. 얼굴 검출을 위한 분류기로서는 SVM(Support Vector Machine)을 사용하였으며 제안한 방법은 ULBP(Uniform Local Binary Pattern)보다 적은 피쳐 개수를 가지면서도 에러율 측면에서, ULBP의 경우보다 나은 성능을 나타내었다.

• ETRI Journal
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• 제33권6호
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• pp.924-934
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• 2011

Sensor networks play an important role in making the dream of ubiquitous computing a reality. With a variety of applications, sensor networks have the potential to influence everyone's life in the near future. However, there are a number of issues in deployment and exploitation of these networks that must be dealt with for sensor network applications to realize such potential. Localization of the sensor nodes, which is the subject of this paper, is one of the basic problems that must be solved for sensor networks to be effectively used. This paper proposes a probabilistic support vector machine (SVM)-based method to gain a fairly accurate localization of sensor nodes. As opposed to many existing methods, our method assumes almost no extra equipment on the sensor nodes. Our experiments demonstrate that the probabilistic SVM method (PSVM) provides a significant improvement over existing localization methods, particularly in sparse networks and rough environments. In addition, a post processing step for PSVM, called attractive/repulsive potential field localization, is proposed, which provides even more improvement on the accuracy of the sensor node locations.

• 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.