• 대한원격탐사학회지
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• 제25권3호
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• pp.233-242
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• 2009

This paper presents two different types of supervised classifiers such as support vector machine (SVM) and spectral angle mapper (SAM). The Compact Airborne Spectrographic Imager (CASI) high resolution aerial image was classified with the above two classifier. The image was classified into eight land use /land cover classes. Accuracy assessment and Kappa statistics were estimated for SVM and SAM separately. The overall classification accuracy and Kappa statistics value of the SAM were 69.0% and 0.62 respectively, which were higher than those of SVM (62.5%, 0.54).

• 한국가시화정보학회지
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• 제19권1호
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• pp.68-73
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• 2021

In this study, the detection of rubber o-ring defects was carried out using k-fold cross validation and Support Vector Machine (SVM) algorithm. The data process was carried out in 3 steps. First, we proceeded with a frame alignment to eliminate unnecessary regions in the learning and secondly, we applied gray-scale changes for computational reduction. Finally, data processing was carried out using image augmentation to prevent data overfitting. After processing data, SVM algorithm was used to obtain normal and defect detection accuracy. In addition, we applied the SVM algorithm through the k-fold cross validation method to compare the classification accuracy. As a result, we obtain results that show better performance by applying the k-fold cross validation method.

• Steel and Composite Structures
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• 제29권3호
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• pp.309-318
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• 2018

Steel girders are the structural members often used for passing long spans. Mostly being subjected to patch loading, or concentrated loading, steel girders are likely to face sudden deformation or damage e.g., web breathing. Horizontal or vertical stiffeners are employed to overcome this phenomenon. This study aims at assessing the feasibility of a machine learning method, namely the support vector machines (SVM) in predicting the patch loading resistance of longitudinally stiffened webs. A database consisting of 162 test data is utilized to develop SVM models and the model with best performance is selected for further inspection. Existing formulations proposed by other researchers are also investigated for comparison. BS5400 and other existing models (model I, model II and model III) appear to yield underestimated predictions with a large scatter; i.e., mean experimental-to-predicted ratios of 1.517, 1.092, 1.155 and 1.256, respectively; whereas the selected SVM model has high prediction accuracy with significantly less scatter. Robust nature and accurate predictions of SVM confirms its feasibility of potential use in solving complex engineering problems.

• 한국경영과학회지
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• 제31권2호
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• pp.69-85
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• 2006

Transductive Support Vector Machine(TSVM) is one of semi-supervised learning algorithms which exploit the domain structure of the whole data by considering labeled and unlabeled data together. Although it was proposed several years ago, there has been no efficient algorithm which can handle problems with more than hundreds of training examples. In this paper, we propose an efficient branch-and-bound algorithm which can solve large-scale TSVM problems with thousands of training examples. The proposed algorithm uses two bounding techniques: min-cut bound and reduced SVM bound. The min-cut bound is derived from a capacitated graph whose cuts represent a lower bound to the optimal objective function value of the dual problem. The reduced SVM bound is obtained by constructing the SVM problem with only labeled data. Experimental results show that the accuracy rate of TSVM can be significantly improved by learning from the optimal solution of TSVM, rather than an approximated solution.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2000년도 가을 학술발표논문집 Vol.27 No.2 (2)
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• pp.458-460
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• 2000

본 논문에서는 유전자 알고리즘(GA)과 Support Vector Machine(SVM)을 결합하여 사용한 얼굴 인식 시스템을 제안한다. 기존의 SVM을 이용한 얼굴 인식 연구에서는 얼굴 전체 영상을 SVM의 입력벡터로 사용하는데 반해, 본 연구에서는 GA를 이용하여 얼굴 영상 중에서 개인별로 식별 능력이 우수한 특징들만을 선택하여 이를 SVM의 입력벡터로 사용한다. 조명, 표정, 안경 착용 등 다양한 변화가 있는 Yale 얼굴 데이터베이스를 사용하여 실험한 결과, 얼굴 전체 영상을 사용한 경우보다 더 좋은 인식률을 보였다. 또한 제안된 방법에 의한 얼굴 인식 시스템은 각 개인별로 식별력이 우수한 특징들만을 저장하므로, 얼굴인식 시스템을 구성하기 위해 저장될 정보의 양이 현저하게 감소하게 된다.

• Asian Journal of Business Environment
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• 제13권4호
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• pp.1-6
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• 2023

Purpose: To explore the application prospect of support vector machine (SVM) in supply chain management and its practical application in supply chain performance evaluation practice. Research design, data and methodology: This paper establishes the performance evaluation index system of supply chain management according to the balanced scorecard (BSC) theory, and establishes the SVM model of supply chain management performance evaluation based on the SVM principle. Results: The performance evaluation results of the supply chain of an electric power equipment Co., Ltd. in Harbin established by using the model are consistent with the actual situation, which indicates the nature and accuracy of the possible reflection of the established supply chain performance evaluation model. Conclusions: The results show that SVM model can be used to evaluate enterprise supply chain management performance indicators, and can improve enterprise supply chain management performance, thus demonstrating the effectiveness of the model.

• 대한전기학회논문지:전력기술부문A
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• 제55권11호
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• pp.485-491
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• 2006

In this paper, we study the problem of model selection for Support Vector Machine(SVM) predictor for short-term load forecasting. The model selection amounts to tuning SVM parameters, such as the cost coefficient C and kernel parameters and so on, in order to maximize the prediction performance of SVM. We propose that Cross-Validation method can be used as a model selection algorithm for SVM-based load forecasting technique. Through the various experiments on several data sets, we found that the difference between the prediction error of SVM using Cross-Validation and that of ideal SVM is less than 5%. This shows that SVM parameters for load forecasting can be efficiently tuned by using Cross-Validation.

• 한국정보통신학회논문지
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• 제17권5호
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• pp.1083-1088
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• 2013

수중 천해 환경에서 능동소나의 반향 신호로 기뢰를 탐지 및 식별하는 일은 복잡한 해양 환경의 영향으로 어려운 문제이다. SVM은 패턴인식 문제에서 최적의 해를 제공하는 이진 분류기이다. 본 논문에서는 SVM을 이용하여 능동소나의 반향 데이터로 기뢰와 같은 금속 물체와 바위를 식별하는 실험을 수행하면서, SVM에 사용되는 커널함수의 파라미터 값의 변화에 따른 식별 성능을 분석하고 제시하였다.

• 한국정보통신학회논문지
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• 제9권7호
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• pp.1569-1574
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• 2005

In this paper, I propose an intelligent credit rating system using a bankruptcy prediction model based on support vector machines (SVMs). SVMs are promising methods because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study examines the feasibility of applying SVM in Predicting corporate bankruptcies by comparing it with other data mining techniques. In addition. this study presents architecture and prototype of intelligeht credit rating systems based on SVM models.

• 제어로봇시스템학회논문지
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• 제16권12호
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• pp.1226-1232
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• 2010

A door can serve as a feature for place classification and localization for navigation of a mobile robot in indoor environments. This paper proposes a door detection method based on the recognition of various door handles using the general Hough transform (GHT) and support vector machine (SVM). The contour and color histogram of a door handle extracted from the database are used in GHT and SVM, respectively. The door recognition scheme consists of four steps. The first step determines the region of interest (ROI) images defined by the color information and the environment around the door handle for stable recognition. In the second step, the door handle is recognized using the GHT method from the ROI image and the image patches are extracted from the position of the recognized door handle. In the third step, the extracted patch is classified whether it is the image patch of a door handle or not using the SVM classifier. The door position is probabilistically determined by the recognized door handle. Experimental results show that the proposed method can recognize various door handles and detect doors in a robust manner.