• Title/Summary/Keyword: 서포트벡터기계

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Analysis of market share attraction data using LS-SVM (최소제곱 서포트벡터기계를 이용한 시장점유율 자료 분석)

  • Park, Hye-Jung
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.5
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    • pp.879-886
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    • 2009
  • The purpose of this article is to present the application of Least Squares Support Vector Machine in analyzing the existing structure of brand. We estimate the parameters of the Market Share Attraction Model using a non-parametric technique for function estimation called Least Squares Support Vector Machine, which allows us to perform even nonlinear regression by constructing a linear regression function in a high dimensional feature space. Estimation by Least Squares Support Vector Machine technique makes it a good candidate for solving the Market Share Attraction Model. To illustrate the performance of the proposed method, we use the car sales data in South Korea's car market.

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An analysis of satisfaction index on computer education of university using kernel machine (커널머신을 이용한 대학의 컴퓨터교육 만족도 분석)

  • Pi, Su-Young;Park, Hye-Jung;Ryu, Kyung-Hyun
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.5
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    • pp.921-929
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    • 2011
  • In Information age, the academic liberal art Computer education course set up goals for promoting computer literacy and for developing the ability to cope actively with in Information Society and for improving productivity and competition among nations. In this paper, we analyze on discovering of decisive property and satisfaction index to have a influence on computer education on university students. As a preprocessing method, the proposed method select optimum property using correlation feature selection of machine learning tool based on Java and then we use multiclass least square support vector machine based on statistical learning theory. After applying that compare with multiclass support vector machine and multiclass least square support vector machine, we can see the fact that the proposed method have a excellent result like multiclass support vector machine in analysis of the academic liberal art computer education satisfaction index data.

Estimation of nonlinear GARCH-M model (비선형 평균 일반화 이분산 자기회귀모형의 추정)

  • Shim, Joo-Yong;Lee, Jang-Taek
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.5
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    • pp.831-839
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    • 2010
  • Least squares support vector machine (LS-SVM) is a kernel trick gaining a lot of popularities in the regression and classification problems. We use LS-SVM to propose a iterative algorithm for a nonlinear generalized autoregressive conditional heteroscedasticity model in the mean (GARCH-M) model to estimate the mean and the conditional volatility of stock market returns. The proposed method combines a weighted LS-SVM for the mean and unweighted LS-SVM for the conditional volatility. In this paper, we show that nonlinear GARCH-M models have a higher performance than the linear GARCH model and the linear GARCH-M model via real data estimations.

Smoothing Kaplan-Meier estimate using monotone support vector regression (단조 서포트벡터기계를 이용한 카플란-마이어 생존함수의 평활)

  • Hwang, Changha;Shim, Jooyong
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.6
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    • pp.1045-1054
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    • 2012
  • Support vector machine is known to be the very useful statistical method in classification and nonlinear function estimation. In this paper we propose a monotone support vector regression (SVR) for the estimation of monotonically decreasing function. The proposed monotone SVR is applied to smooth the Kaplan-Meier estimate of survival function. Experimental results are then presented which indicate the performance of the proposed monotone SVR using survival functions obtained by exponential distribution.

Combination of Value-at-Risk Models with Support Vector Machine (서포트벡터기계를 이용한 VaR 모형의 결합)

  • Kim, Yong-Tae;Shim, Joo-Yong;Lee, Jang-Taek;Hwang, Chang-Ha
    • Communications for Statistical Applications and Methods
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    • v.16 no.5
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    • pp.791-801
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    • 2009
  • Value-at-Risk(VaR) has been used as an important tool to measure the market risk. However, the selection of the VaR models is controversial. This paper proposes VaR forecast combinations using support vector machine quantile regression instead of selecting a single model out of historical simulation and GARCH.

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.

Online abnormal events detection with online support vector machine (온라인 서포트벡터기계를 이용한 온라인 비정상 사건 탐지)

  • Park, Hye-Jung
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.2
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    • pp.197-206
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    • 2011
  • The ability to detect online abnormal events in signals is essential in many real-world signal processing applications. In order to detect abnormal events, previously known algorithms require an explicit signal statistical model, and interpret abnormal events as statistical model abrupt changes. In general, maximum likelihood and Bayesian estimation theory to estimate well as detection methods have been used. However, the above-mentioned methods for robust and tractable model, it is not easy to estimate. More freedom to estimate how the model is needed. In this paper, we investigate a machine learning, descriptor-based approach that does not require a explicit descriptors statistical model, based on support vector machines are known to be robust statistical models and a sequential optimal algorithm online support vector machine is introduced.

Quantile regression using asymmetric Laplace distribution (비대칭 라플라스 분포를 이용한 분위수 회귀)

  • Park, Hye-Jung
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.6
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    • pp.1093-1101
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    • 2009
  • Quantile regression has become a more widely used technique to describe the distribution of a response variable given a set of explanatory variables. This paper proposes a novel modelfor quantile regression using doubly penalized kernel machine with support vector machine iteratively reweighted least squares (SVM-IRWLS). To make inference about the shape of a population distribution, the widely popularregression, would be inadequate, if the distribution is not approximately Gaussian. We present a likelihood-based approach to the estimation of the regression quantiles that uses the asymmetric Laplace density.

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Estimation of software project effort with genetic algorithm and support vector regression (유전 알고리즘 기반의 서포트 벡터 회귀를 이용한 소프트웨어 비용산정)

  • Kwon, Ki-Tae;Park, Soo-Kwon
    • The KIPS Transactions:PartD
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    • v.16D no.5
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    • pp.729-736
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    • 2009
  • The accurate estimation of software development cost is important to a successful development in software engineering. Until recent days, the model using regression analysis based on statistical algorithm and machine learning method have been used. However, this paper estimates the software cost using support vector regression, a sort of machine learning technique. Also, it finds the best set of optimized parameters applying genetic algorithm. The proposed GA-SVR model outperform some recent results reported in the literature.