• Title/Summary/Keyword: Support Vector Machine

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Quadratic Loss Support Vector Interval Regression Machine for Crisp Input-Output Data

  • Hwang, Chang-Ha
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.2
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    • pp.449-455
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    • 2004
  • Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval regression models for crisp input-output data. The proposed method is based on quadratic loss SVM, which implements quadratic programming approach giving more diverse spread coefficients than a linear programming one. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.

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Power System Voltage Stability Classification Using Interior Point Method Based Support Vector Machine(IPMSVM)

  • Song, Hwa-Chang;Dosano, Rodel D.;Lee, Byong-Jun
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.9 no.3
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    • pp.238-243
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    • 2009
  • This paper present same thodology for the classification of power system voltage stability, the trajectory of which to instability is monotonic, using an interior point method based support vector machine(IPMSVM). The SVM based voltage stability classifier canp rovide real-time stability identification only using the local measurement data, without the topological information conventionally used.

Improvement of rotor flux estimation performance of induction motor using Support Vector Machine $\epsilon$-insensitive Regression Method (Support Vector Machine $\epsilon$-insensitive Regression방법을 이용한 유도전동기의 회전자 자속추정 성능개선)

  • Han, Dong-Chang;Baek, Un-Jae;Kim, Seong-Rak;Park, Ju-Hyeon;Lee, Seok-Gyu;Park, Jeong-Il
    • Proceedings of the KIEE Conference
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    • 2003.11b
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    • pp.43-46
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    • 2003
  • In this paper, a novel rotor flux estimation method of an induction motor using support vector machine(SVM) is presented. Two veil-known different flux models with respect to voltage and current are necessary to estimate the rotor flux of an induction motor. The theory of the SVM algorithm is based on statistical teaming theory. Training of SVH leads to a quadratic programming(QP) problem. The proposed SVM rotor flux estimator guarantees the improvement of performance in the transient and steady state in spite of parameter variation circumstance. The validity and the usefulness of Proposed algorithm are throughly verified through numerical simulation.

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Bankruptcy Prediction using Support Vector Machines (Support Vector Machine을 이용한 기업부도예측)

  • Park, Jung-Min;Kim, Kyoung-Jae;Han, In-Goo
    • 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.

Model of Least Square Support Vector Machine (LSSVM) for Prediction of Fracture Parameters of Concrete

  • Kulkrni, Kallyan S.;Kim, Doo-Kie;Sekar, S.K.;Samui, Pijush
    • International Journal of Concrete Structures and Materials
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    • v.5 no.1
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    • pp.29-33
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    • 2011
  • This article employs Least Square Support Vector Machine (LSSVM) for determination of fracture parameters of concrete: critical stress intensity factor ($K_{Ic}^s$) and the critical crack tip opening displacement ($CTOD_c$). LSSVM that is firmly based on the theory of statistical learning theory uses regression technique. The results are compared with a widely used Artificial Neural Network (ANN) Models of LSSVM have been developed for prediction of $K_{Ic}^s$ and $CTOD_c$, and then a sensitivity analysis has been performed to investigate the importance of the input parameters. Equations have been also developed for determination of $K_{Ic}^s$ and $CTOD_c$. The developed LSSVM also gives error bar. The results show that the developed model of LSSVM is very predictable in order to determine fracture parameters of concrete.

Support Vector Bankruptcy Prediction Model with Optimal Choice of RBF Kernel Parameter Values using Grid Search (Support Vector Machine을 이용한 부도예측모형의 개발 -격자탐색을 이용한 커널 함수의 최적 모수 값 선정과 기존 부도예측모형과의 성과 비교-)

  • Min Jae H.;Lee Young-Chan
    • Journal of the Korean Operations Research and Management Science Society
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    • v.30 no.1
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    • pp.55-74
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    • 2005
  • Bankruptcy prediction has drawn a lot of research interests in previous literature, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper employs a relatively new machine learning technique, support vector machines (SVMs). to bankruptcy prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use grid search technique using 5-fold cross-validation to find out the optimal values of the parameters of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM. we compare its performance with multiple discriminant analysis (MDA), logistic regression analysis (Logit), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.

A Fault Detection of Cyclic Signals Using Support Vector Machine-Regression (Support Vector Machine-Regression을 이용한 주기신호의 이상탐지)

  • Park, Seung-Hwan;Kim, Jun-Seok;Park, Cheong-Sool;Kim, Sung-Shick;Baek, Jun-Geol
    • Journal of Korean Society for Quality Management
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    • v.38 no.3
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    • pp.354-362
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    • 2010
  • This paper presents a non-linear control chart based on support vector machine regression (SVM-R) to improve the accuracy of fault detection of cyclic signals. The proposed algorithm consists of the following two steps. First, the center line of the control chart is constructed by using SVM-R. Second, we calculate control limits by variances that are estimated by perpendicular and normal line of the center line. For performance evaluation, we apply proposed algorithm to the industrial data of the chemical vapor deposition process which is one of the semiconductor processes. The proposed method has better fault detection performance than other existing method

Sensor Data Allocation using Support Vector Machine in Distributed-Gateway System (분산 게이트웨이 환경에서의 Support Vector Machine을 이용한 센서 데이터 할당)

  • Lee, Tae-Ho;Yoo, Seung-Eon;Lee, Byung-Jun;Kim, Kyung-Tae;Youn, Hee-Yong
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2018.07a
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    • pp.199-200
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    • 2018
  • 본 논문에서는 IIoT(Industrial IoT) 환경의 분산 게이트웨이 시스템(Distributed-Gateway System)에서 사용되는 수 천 개 이상의 센서에서 데이터 전송을 받는 각 게이트웨이의 데이터 처리 속도를 향상시키고 작업 오류를 줄이기 위하여 SVM(Support Vector Machine) 알고리즘을 사용한 센서 데이터 할당 기법을 제안한다. 각 센서의 반복 측정 간격과 중요도에 따라 작업부하(Workload)를 구하고, 이를 순차 반복 비교를 통해 Sub-task 값을 구한다. 이렇게 구해진 Sub-task값을 기준으로 각 게이트웨이에 할당시킴으로써 신뢰성과 정확성, 신속성을 확보한다.

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A Study on Customer Segmentation Prediction Model using Support Vector Machine (Support Vector Machine을 이용한 고객이탈 예측모형에 관한 연구)

  • Seo Kwang Kyu
    • Journal of the Korea Safety Management & Science
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    • v.7 no.1
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    • pp.199-210
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    • 2005
  • Customer segmentation prediction has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. However, ANN approaches have suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learning technique, support vector machines (SVM), to the customer segmentation prediction problem in an attempt to provide a model with better explanatory power. To evaluate the prediction accuracy of SVM, we compare its performance with logistic regression analysis and ANN. The experiment results with real data of insurance company show that SVM superiors to them.

A Study on Predicting Construction Cost of Educational Building Project at early stage Using Support Vector Machine Technique (서포트벡터머신을 이용한 교육시설 초기 공사비 예측에 관한 연구)

  • Shin, Jae-Min;Kim, Gwang-Hee
    • The Journal of Sustainable Design and Educational Environment Research
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    • v.11 no.3
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    • pp.46-54
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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 various of techniques are developed to predict the construction cost accurately and expeditely. Among the techniques, Support Vector Machine(SVM) has an excellent ability for generalization performance. Therefore, the purpose of this study is to construct the prediction model for construction cost of educational building project using support vector machine technique. And to verify the accuracy of prediction model for construction cost. The performance data used in this study are 217 school building project cost which have been completed from 2004 to 2007 in Gyeonggi-Do, Korea. The result shows that average error rate was 7.48% for SVM prediction model. So using SVM model on predicting construction cost of educational building project will be a considerably effective way at the early project stage.