• 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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    • 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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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.

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.

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.

Expected shortfall estimation using kernel machines

  • Shim, Jooyong;Hwang, Changha
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.3
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    • pp.625-636
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    • 2013
  • In this paper we study four kernel machines for estimating expected shortfall, which are constructed through combinations of support vector quantile regression (SVQR), restricted SVQR (RSVQR), least squares support vector machine (LS-SVM) and support vector expectile regression (SVER). These kernel machines have obvious advantages such that they achieve nonlinear model but they do not require the explicit form of nonlinear mapping function. Moreover they need no assumption about the underlying probability distribution of errors. Through numerical studies on two artificial an two real data sets we show their effectiveness on the estimation performance at various confidence levels.

Fine-tuning SVM for Enhancing Speech/Music Classification (SVM의 미세조정을 통한 음성/음악 분류 성능향상)

  • Lim, Chung-Soo;Song, Ji-Hyun;Chang, Joon-Hyuk
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.48 no.2
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    • pp.141-148
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    • 2011
  • Support vector machines have been extensively studied and utilized in pattern recognition area for years. One of interesting applications of this technique is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel approach that improves the speech/music classification of support vector machines. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. We first analyze the impact of kernel width parameter on the classifications made by support vector machines. From this analysis, we observe that we can fine-tune outputs of support vector machines with the kernel width parameter. To make the most of this capability, we identify strong correlation among neighboring input frames, and use this correlation information as a guide to adjusting kernel width parameter. According to the experimental results, the proposed algorithm is found to have potential for improving the performance of support vector machines.

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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    • 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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A Web Recommendation System using Grid based Support Vector Machines

  • Jun, Sung-Hae
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.7 no.2
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    • pp.91-95
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    • 2007
  • Main goal of web recommendation system is to study how user behavior on a website can be predicted by analyzing web log data which contain the visited web pages. Many researches of the web recommendation system have been studied. To construct web recommendation system, web mining is needed. Especially, web usage analysis of web mining is a tool for recommendation model. In this paper, we propose web recommendation system using grid based support vector machines for improvement of web recommendation system. To verify the performance of our system, we make experiments using the data set from our web server.

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
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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.

Improving the Generalization Error Bound using Total margin in Support Vector Machines (서포트 벡터 기계에서 TOTAL MARGIN을 이용한 일반화 오차 경계의 개선)

  • Yoon, Min
    • The Korean Journal of Applied Statistics
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    • v.17 no.1
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    • pp.75-88
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    • 2004
  • The Support Vector Machine(SVM) algorithm has paid attention on maximizing the shortest distance between sample points and discrimination hyperplane. This paper suggests the total margin algorithm which considers the distance between all data points and the separating hyperplane. The method extends existing support vector machine algorithm. In addition, this newly proposed method improves the generalization error bound. Numerical experiments show that the total margin algorithm provides good performance, comparing with the previous methods.