• Title, Summary, Keyword: SVM(Support Vector Machine)

Search Result 1,072, Processing Time 0.037 seconds

Fuzzy One Class Support Vector Machine (퍼지 원 클래스 서포트 벡터 머신)

  • Kim, Ki-Joo;Choi, Young-Sik
    • Journal of Internet Computing and Services
    • /
    • v.6 no.3
    • /
    • pp.159-170
    • /
    • 2005
  • OC-SVM(One Class Support Vector Machine) avoids solving a full density estimation problem, and instead focuses on a simpler task, estimating quantiles of a data distribution, i.e. its support. OC-SVM seeks to estimate regions where most of data resides and represents the regions as a function of the support vectors, Although OC-SVM is powerful method for data description, it is difficult to incorporate human subjective importance into its estimation process, In order to integrate the importance of each point into the OC-SVM process, we propose a fuzzy version of OC-SVM. In FOC-SVM (Fuzzy One-Class Support Vector Machine), we do not equally treat data points and instead weight data points according to the importance measure of the corresponding objects. That is, we scale the kernel feature vector according to the importance measure of the object so that a kernel feature vector of a less important object should contribute less to the detection process of OC-SVM. We demonstrate the performance of our algorithm on several synthesized data sets, Experimental results showed the promising results.

  • PDF

Development of Fuzzy Support Vector Machine and Evaluation of Performance Using Ionosphere Radar Data (Fuzzy Twin Support Vector Machine 개발 및 전리층 레이더 데이터를 통한 성능 평가)

  • Cheon, Min-Kyu;Yoon, Chang-Yong;Kim, Eun-Tai;Park, Mig-Non
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.18 no.4
    • /
    • pp.549-554
    • /
    • 2008
  • Support Vector machine is the classifier which is based on the statistical training theory. Twin Support Vector Machine(TWSVM) is a kind of binary classifier that determines two nonparallel planes by solving two related SVM-type problems. The training time of TWSVM is shorter than that of SVM, but TWSVM doesn't shows worse performance than that of SVM. This paper proposes the TWSVM which is applied fuzzy membership, and compares the performance of this classifier with the other classifiers using Ionosphere radar data set.

Prediction Performance of Hybrid Least Square Support Vector Machine with First Principle Knowledge (First Principle을 결합한 최소제곱 Support Vector Machine의 예측 능력)

  • 김병주;심주용;황창하;김일곤
    • Journal of KIISE:Software and Applications
    • /
    • v.30 no.7_8
    • /
    • pp.744-751
    • /
    • 2003
  • A hybrid least square Support Vector Machine combined with First Principle(FP) knowledge is proposed. We compare hybrid least square Support Vector Machine(HLS-SVM) with early proposed models such as Hybrid Neural Network(HNN) and HNN with Extended Kalman Filter(HNN-EKF). In the training and validation stage HLS-SVM shows similar performance with HNN-EKF but better than HNN, whereas, in the testing stage, it shows three times better than HNN-EKF, hundred times better than HNN model.

Weighted Support Vector Machines for Heteroscedastic Regression

  • Park, Hye-Jung;Hwang, Chang-Ha
    • Journal of the Korean Data and Information Science Society
    • /
    • v.17 no.2
    • /
    • pp.467-474
    • /
    • 2006
  • In this paper we present a weighted support vector machine(SVM) and a weighted least squares support vector machine(LS-SVM) for the prediction in the heteroscedastic regression model. By adding weights to standard SVM and LS-SVM the better fitting ability can be achieved when errors are heteroscedastic. In the numerical studies, we illustrate the prediction performance of the proposed procedure by comparing with the procedure which combines standard SVM and LS-SVM and wild bootstrap for the prediction.

  • PDF

Development of Fuzzy Support Vector Machine for Pattern Classification (패턴 분류를 위한 Fuzzy Twin Support Vector machine 개발)

  • Cheon, Min-Gyu;Yun, Chang-Yong;Kim, Eun-Tae;Park, Min-Yong
    • Proceedings of the Korean Institute of Intelligent Systems Conference
    • /
    • /
    • pp.279-282
    • /
    • 2007
  • Support Vector Machine(SVM)은 통계적 학습 이론에 기반을 둔 분류기이다. 또한 Twin Support Vector Machine(TWSVM)은 이진 SVM 분류기의 한 종류로써, 서로 관련된 두 개의 SVM 유형 문제를 통해 평행하지 않은 두 개의 평면을 결정하고 이 두 평면을 통해 분류기를 완성하는 방식이다. 이러한 방식은 TWSVM은 학습 시간이 SVM에 비해 훨씬 짧으며, SVM과 비교하여 떨어지지 않는 성능을 보여준다. 본 논문은 분류기 입력에 Fuzzy Memvership을 적용하는 방식의 TWSVM을 제안하고, 2차원 벡터 입력에 대한 실험을 통하여 기존에 제시 되었던 TWSVM과 비교한다.

  • PDF

A transductive least squares support vector machine with the difference convex algorithm

  • Shim, Jooyong;Seok, Kyungha
    • Journal of the Korean Data and Information Science Society
    • /
    • v.25 no.2
    • /
    • pp.455-464
    • /
    • 2014
  • Unlabeled examples are easier and less expensive to obtain than labeled examples. Semisupervised approaches are used to utilize such examples in an eort to boost the predictive performance. This paper proposes a novel semisupervised classication method named transductive least squares support vector machine (TLS-SVM), which is based on the least squares support vector machine. The proposed method utilizes the dierence convex algorithm to derive nonconvex minimization solutions for the TLS-SVM. A generalized cross validation method is also developed to choose the hyperparameters that aect the performance of the TLS-SVM. The experimental results conrm the successful performance of the proposed TLS-SVM.

Concurrent Support Vector Machine Processor (Concurrent Support Vector Machine 프로세서)

  • 위재우;이종호
    • The Transactions of the Korean Institute of Electrical Engineers D
    • /
    • v.53 no.8
    • /
    • pp.578-584
    • /
    • 2004
  • The CSVM(Current Support Vector Machine) that is a digital architecture performing all phases of recognition process including kernel computing, learning, and recall of SVM(Support Vector Machine) on a chip is proposed. Concurrent operation by parallel architecture of elements generates high speed and throughput. The classification problems of bio data having high dimension are solved fast and easily using the CSVM. Quadratic programming in original SVM learning algorithm is not suitable for hardware implementation, due to its complexity and large memory consumption. Hardware-friendly SVM learning algorithms, kernel adatron and kernel perceptron, are embedded on a chip. Experiments on fixed-point algorithm having quantization error are performed and their results are compared with floating-point algorithm. CSVM implemented on FPGA chip generates fast and accurate results on high dimensional cancer data.

A Differential Evolution based Support Vector Clustering (차분진화 기반의 Support Vector Clustering)

  • Jun, Sung-Hae
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.17 no.5
    • /
    • pp.679-683
    • /
    • 2007
  • Statistical learning theory by Vapnik consists of support vector machine(SVM), support vector regression(SVR), and support vector clustering(SVC) for classification, regression, and clustering respectively. In this algorithms, SVC is good clustering algorithm using support vectors based on Gaussian kernel function. But, similar to SVM and SVR, SVC needs to determine kernel parameters and regularization constant optimally. In general, the parameters have been determined by the arts of researchers and grid search which is demanded computing time heavily. In this paper, we propose a differential evolution based SVC(DESVC) which combines differential evolution into SVC for efficient selection of kernel parameters and regularization constant. To verify improved performance of our DESVC, we make experiments using the data sets from UCI machine learning repository and simulation.

A Recent Development in Support Vector Machine Classification

  • Hong, Dug-Hun;Hwang, Chang-Ha;Na, Eun-Young
    • 한국데이터정보과학회:학술대회논문집
    • /
    • /
    • pp.23-28
    • /
    • 2002
  • Support vector machine(SVM) has been very successful in classification, regression, time series prediction and density estimation. In this paper, we will propose SVM for fuzzy data classification.

  • PDF

Design of Robust Support Vector Machine Using Genetic Algorithm (유전자 알고리즘을 이용한 강인한 Support vector machine 설계)

  • Lee, Hee-Sung;Hong, Sung-Jun;Lee, Byung-Yun;Kim, Eun-Tai
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.20 no.3
    • /
    • pp.375-379
    • /
    • 2010
  • The support vector machine (SVM) has been widely used in variety pattern recognition problems applicable to recommendation systems due to its strong theoretical foundation and excellent empirical successes. However, SVM is sensitive to the presence of outliers since outlier points can have the largest margin loss and play a critical role in determining the decision hyperplane. For robust SVM, we limit the maximum value of margin loss which includes the non-convex optimization problem. Therefore, we proposed the design method of robust SVM using genetic algorithm (GA) which can solve the non-convex optimization problem. To demonstrate the performance of the proposed method, we perform experiments on various databases selected in UCI repository.