• Title, Summary, Keyword: support function

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Performance Analysis of Kernel Function for Support Vector Machine (Support Vector Machine에 대한 커널 함수의 성능 분석)

  • Sim, Woo-Sung;Sung, Se-Young;Cheng, Cha-Keon
    • Proceedings of the IEEK Conference
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    • pp.405-407
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    • 2009
  • SVM(Support Vector Machine) is a classification method which is recently watched in mechanical learning system. Vapnik, Osuna, Platt etc. had suggested methodology in order to solve needed QP(Quadratic Programming) to realize SVM so that have extended application field. SVM find hyperplane which classify into 2 class by converting from input space converter vector to characteristic space vector using Kernel Function. This is very systematic and theoretical more than neural network which is experiential study method. Although SVM has superior generalization characteristic, it depends on Kernel Function. There are three category in the Kernel Function as Polynomial Kernel, RBF(Radial Basis Function) Kernel, Sigmoid Kernel. This paper has analyzed performance of SVM against kernel using virtual data.

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Weighted Support Vector Machines with the SCAD Penalty

  • Jung, Kang-Mo
    • Communications for Statistical Applications and Methods
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    • v.20 no.6
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    • pp.481-490
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    • 2013
  • Classification is an important research area as data can be easily obtained even if the number of predictors becomes huge. The support vector machine(SVM) is widely used to classify a subject into a predetermined group because it gives sound theoretical background and better performance than other methods in many applications. The SVM can be viewed as a penalized method with the hinge loss function and penalty functions. Instead of $L_2$ penalty function Fan and Li (2001) proposed the smoothly clipped absolute deviation(SCAD) satisfying good statistical properties. Despite the ability of SVMs, they have drawbacks of non-robustness when there are outliers in the data. We develop a robust SVM method using a weight function with the SCAD penalty function based on the local quadratic approximation. We compare the performance of the proposed SVM with the SVM using the $L_1$ and $L_2$ penalty functions.

Analysis of the Influence of Physical Function and Social Support on Depressive Symptom in the Community Elderly Using the Structural Equation Model (구조방정식모형을 이용한 노인들의 신체적 기능과 사회적지지가 우울수준에 미치는 영향 분석)

  • Shin, Eun-Sook;Kwon, In-Sun;Cho, Young-Chae
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.12 no.11
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    • pp.4995-5004
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    • 2011
  • This study was performed to determine the levels of depressive symptoms among community elderlies and to reveal its related factors, specifically aimed at revealing factors such as social support, family support and physical function. The interviews were performed, during the period from April 1st, to June 30th, 2010, to 995 elderlies in Daejeon city. As a results, social support, family support, ADL and IADL was found to be in a positive correlation with depressive symptoms. With the analysis of covariance structure, social support was more influential on the level of depressive symptom than family support and physical function. It was found to have the inter-relational effects that the greater the social support, family support and physical function, the lower the level of depressive symptoms.

Sparse kernel classication using IRWLS procedure

  • Kim, Dae-Hak
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.4
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    • pp.749-755
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    • 2009
  • Support vector classification (SVC) provides more complete description of the lin-ear and nonlinear relationships between input vectors and classifiers. In this paper. we propose the sparse kernel classifier to solve the optimization problem of classification with a modified hinge loss function and absolute loss function, which provides the efficient computation and the sparsity. We also introduce the generalized cross validation function to select the hyper-parameters which affects the classification performance of the proposed method. Experimental results are then presented which illustrate the performance of the proposed procedure for classification.

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Support vector expectile regression using IRWLS procedure

  • Choi, Kook-Lyeol;Shim, Jooyong;Seok, Kyungha
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.4
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    • pp.931-939
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    • 2014
  • In this paper we propose the iteratively reweighted least squares procedure to solve the quadratic programming problem of support vector expectile regression with an asymmetrically weighted squares loss function. The proposed procedure enables us to select the appropriate hyperparameters easily by using the generalized cross validation function. Through numerical studies on the artificial and the real data sets we show the effectiveness of the proposed method on the estimation performances.

Incremental Support Vector Learning Method for Function Approximation (함수 근사를 위한 점증적 서포트 벡터 학습 방법)

  • 임채환;박주영
    • Proceedings of the IEEK Conference
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    • pp.135-138
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    • 2002
  • This paper addresses incremental learning method for regression. SVM(support vector machine) is a recently proposed learning method. In general training a support vector machine requires solving a QP (quadratic programing) problem. For very large dataset or incremental dataset, solving QP problems may be inconvenient. So this paper presents an incremental support vector learning method for function approximation problems.

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Design of controller using Support Vector Regression (서포트 벡터 회귀를 이용한 제어기 설계)

  • Hwang, Ji-Hwan;Kwak, Hwan-Joo;Park, Gwi-Tae
    • Proceedings of the IEEK Conference
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    • pp.320-322
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    • 2009
  • Support vector learning attracts great interests in the areas of pattern classification, function approximation, and abnormality detection. In this pater, we design the controller using support vector regression which has good properties in comparison with multi-layer perceptron or radial basis function. The applicability of the presented method is illustrated via an example simulation.

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Iterative Support Vector Quantile Regression for Censored Data

  • Shim, Joo-Yong;Hong, Dug-Hun;Kim, Dal-Ho;Hwang, Chang-Ha
    • Communications for Statistical Applications and Methods
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    • v.14 no.1
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    • pp.195-203
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    • 2007
  • In this paper we propose support vector quantile regression (SVQR) for randomly right censored data. The proposed procedure basically utilizes iterative method based on the empirical distribution functions of the censored times and the sample quantiles of the observed variables, and applies support vector regression for the estimation of the quantile function. Experimental results we then presented to indicate the performance of the proposed procedure.

Empirical Choice of the Shape Parameter for Robust Support Vector Machines

  • Pak, Ro-Jin
    • Communications for Statistical Applications and Methods
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    • v.15 no.4
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    • pp.543-549
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    • 2008
  • Inspired by using a robust loss function in the support vector machine regression to control training error and the idea of robust template matching with M-estimator, Chen (2004) applies M-estimator techniques to gaussian radial basis functions and form a new class of robust kernels for the support vector machines. We are specially interested in the shape of the Huber's M-estimator in this context and propose a way to find the shape parameter of the Huber's M-estimating function. For simplicity, only the two-class classification problem is considered.

Central Control over Distributed Service Function Path

  • Li, Dan;Lan, Julong;Hu, Yuxiang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.2
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    • pp.577-594
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    • 2020
  • Service Function Chaining (SFC) supports services through linking an ordered list of functions. There may be multiple instances of the same function, which provides a challenge to select available instances for all the functions in an SFC and generate a specific Service Function Path (SFP). Aiming to solve the problem of SFP selection, we propose an architecture consisting of distributed SFP algorithm and central control mechanism. Nodes generate distributed routings based on the first function and destination node in each service request. Controller supervises all of the distributed routing tables and modifies paths as required. The architecture is scalable, robust and quickly reacts to failures because of distributed routings. Besides, it enables centralized and direct control of the forwarding behavior with the help of central control mechanism. Simulation results show that distributed routing tables can generate efficient SFP and the average cost is acceptable. Compared with other algorithms, our design has a good performance on average cost of paths and load balancing, and the response delay to service requests is much lower.