• Title/Summary/Keyword: Support vector machines

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An Application of Support Vector Machines for Fault Diagnosis

  • Hai Pham Minh;Phuong Tu Minh
    • Proceedings of the IEEK Conference
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    • summer
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    • pp.371-375
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    • 2004
  • Fault diagnosis is one of the most studied problems in process engineering. Recently, great research interest has been devoted to approaches that use classification methods to detect faults. This paper presents an application of a newly developed classification method - support vector machines - for fault diagnosis in an industrial case. A real set of operation data of a motor pump was used to train and test the support vector machines. The experiment results show that the support vector machines give higher correct detection rate of faults in comparison to rule-based diagnostics. In addition, the studied method can work with fewer training instances, what is important for online diagnostics.

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An analysis of Speech Acts for Korean Using Support Vector Machines (지지벡터기계(Support Vector Machines)를 이용한 한국어 화행분석)

  • En Jongmin;Lee Songwook;Seo Jungyun
    • The KIPS Transactions:PartB
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    • v.12B no.3 s.99
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    • pp.365-368
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    • 2005
  • We propose a speech act analysis method for Korean dialogue using Support Vector Machines (SVM). We use a lexical form of a word, its part of speech (POS) tags, and bigrams of POS tags as sentence features and the contexts of the previous utterance as context features. We select informative features by Chi square statistics. After training SVM with the selected features, SVM classifiers determine the speech act of each utterance. In experiment, we acquired overall $90.54\%$ of accuracy with dialogue corpus for hotel reservation domain.

A Convex Cluster Merging Algorithm using Support Vector Machines (Support Vector Machines를 이용한 Convex 클러스터 결합 알고리즘)

  • 최병인;이정훈
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2002.12a
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    • pp.267-270
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    • 2002
  • 본 논문에서는 Support Vector Machines (SVM) 을 이용하여, 빠르고 정확한 두 convex한 클러스터 간의 거리 측정 방법을 제시한다 제시된 방법에서는, SVM에 의해서 생성되는 최적 다차원 평면이 두 클러스터간의 최소 거리를 계산하는데 사용된다. 또한, 본 논문에서는 이러한 두 클러스터 간의 최적의 거리를 사용하여, Fuzzy Convex Clustering (FCC) 방법 (1) 에 의해서 생성되는 Convex 클러스터들을 묶어주는 효과적인 클러스터 결합 알고리즘을 제시하였다. 그러므로, 데이터의 부적절한 표현을 유발하지 않고도 클러스터들의 개수를 좀 더 줄일 수 있었다. 제시한 방법의 타당성을 위하여 여러 실험 결과를 제시하였다

Development of Intelligent Credit Rating System using Support Vector Machines (Support Vector Machine을 이용한 지능형 신용평가시스템 개발)

  • Kim Kyoung-jae
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.9 no.7
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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.

Estimating global solar radiation using wavelet and data driven techniques

  • Kim, Sungwon;Seo, Youngmin
    • Proceedings of the Korea Water Resources Association Conference
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    • 2015.05a
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    • pp.475-478
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    • 2015
  • The objective of this study is to apply a hybrid model for estimating solar radiation and investigate their accuracy. A hybrid model is wavelet-based support vector machines (WSVMs). Wavelet decomposition is employed to decompose the solar radiation time series into approximation and detail components. These decomposed time series are then used as inputs of support vector machines (SVMs) modules in the WSVMs model. Results obtained indicate that WSVMs can successfully be used for the estimation of daily global solar radiation at Champaign and Springfield stations in Illinois.

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Modeling properties of self-compacting concrete: support vector machines approach

  • Siddique, Rafat;Aggarwal, Paratibha;Aggarwal, Yogesh;Gupta, S.M.
    • Computers and Concrete
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    • v.5 no.5
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    • pp.461-473
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    • 2008
  • The paper explores the potential of Support Vector Machines (SVM) approach in predicting 28-day compressive strength and slump flow of self-compacting concrete. Total of 80 data collected from the exiting literature were used in present work. To compare the performance of the technique, prediction was also done using a back propagation neural network model. For this data-set, RBF kernel worked well in comparison to polynomial kernel based support vector machines and provide a root mean square error of 4.688 (MPa) (correlation coefficient=0.942) for 28-day compressive strength prediction and a root mean square error of 7.825 cm (correlation coefficient=0.931) for slump flow. Results obtained for RMSE and correlation coefficient suggested a comparable performance by Support Vector Machine approach to neural network approach for both 28-day compressive strength and slump flow prediction.

Support Vector Machine based Cluster Merging (Support Vector Machines 기반의 클러스터 결합 기법)

  • Choi, Byung-In;Rhee, Frank Chung-Hoon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.14 no.3
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    • pp.369-374
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    • 2004
  • A cluster merging algorithm that merges convex clusters resulted by the Fuzzy Convex Clustering(FCC) method into non-convex clusters was proposed. This was achieved by proposing a fast and reliable distance measure between two convex clusters using Support Vector Machines(SVM) to improve accuracy and speed over other existing conventional methods. In doing so, it was possible to reduce cluster number without losing its representation of the data. In this paper, results for several data sets are given to show the validity of our distance measure and algorithm.

Comments Classification System using Support Vector Machines and Topic Signature (지지 벡터 기계와 토픽 시그너처를 이용한 댓글 분류 시스템 언어에 독립적인 댓글 분류 시스템)

  • Bae, Min-Young;En, Ji-Hyun;Jang, Du-Sung;Cha, Jeong-Won
    • 한국HCI학회:학술대회논문집
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    • 2009.02a
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    • pp.263-266
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    • 2009
  • Comments are short and not use spacing words or comma more than general document. We convert the 7-gram into 3-gram and select key features using topic signature. Topic signature is widely used for selecting features in document classification and summarization. We use the SVM(Support Vector Machines) as a classifier. From the result of experiments, we can see that the proposed method is outstanding over the previous methods. The proposed system can also apply to other languages.

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A Comparison Study on Back-Propagation Neural Network and Support Vector Machines for the Image Classification Problems (영상분류문제를 위한 역전파 신경망과 Support Vector Machines의 비교 연구)

  • Seo, Kwang-Kyu
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.9 no.6
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    • pp.1889-1893
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    • 2008
  • This paper explores the classification performance of applying to support vector machines (SVMs) for the image classification problems. In this study, we extract the color, texture and shape features of natural images and compare the performance of image classification using each individual feature and integrated features. The experiment results show that classification accuracy on the basis of color feature is better than that based on texture and shape features and the results of the integrating features also provides a better and more robust performance than individual feature. In additions, we show that the proposed classifier of SVM based approach outperforms BPNN to corporate the image classification problems.