• The Korean Journal of Applied Statistics
• /
• v.21 no.1
• /
• pp.159-168
• /
• 2008

Since the term structure of interest rates (TSIR) has longitudinal data, we should consider as input variables both time left to maturity and time simultaneously to get a more useful and more efficient function estimation. However, since the resulting data set becomes very large, we need to develop a fast and reliable estimation method for large data set. Furthermore, it tends to overestimate TSIR because data are correlated. To solve these problems we propose a mixture of weighted least squares support vector machines. We recognize that the estimate is well smoothed and well explains effects of the third stock market crash in USA through applying the proposed method to the US Treasury bonds data.

• Communications for Statistical Applications and Methods
• /
• v.15 no.1
• /
• pp.125-136
• /
• 2008

In many practical engineering design problems, the form of objective functions is not given explicitly in terms of design variables. Given the value of design variables, under this circumstance, the value of objective functions is obtained by real/computational experiments such as structural analysis, fluid mechanic analysis, thermodynamic analysis, and so on. These experiments are, in general, considerably expensive. In order to make the number of these experiments as few as possible, optimization is performed in parallel with predicting the form of objective functions. Response Surface Methods (RSM) are well known along this approach. This paper suggests to apply Support Vector Machines (SVM) for predicting the objective functions. One of most important tasks in this approach is to allocate sample data moderately in order to make the number of experiments as small as possible. It will be shown that the information of support vector can be used effectively to this aim. The effectiveness of our suggested method will be shown through numerical example which is well known in design of engineering.

• The Korean Journal of Applied Statistics
• /
• v.18 no.2
• /
• pp.421-434
• /
• 2005

Machine learning has been extensively studied in recent years as effective tools in pattern classification problem. Although there have been several approaches to machine learning, we focus on the mathematical programming (in particular, multi-objective and goal programming; MOP/GP) approaches in this paper. Among them, Support Vector Machine (SVM) is gaining much popularity recently. In pattern classification problem with two class sets, the idea is to find a maximal margin separating hyperplane which gives the greatest separation between the classes in a high dimensional feature space. However, the idea of maximal margin separation is not quite new: in 1960's the multi-surface method (MSM) was suggested by Mangasarian. In 1980's, linear classifiers using goal programming were developed extensively. This paper proposes a new family of SVM using MOP/GP techniques, and discusses its effectiveness throughout several numerical experiments.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.3
• /
• pp.266-274
• /
• 2003

This paper considers an incremental support vector learning for the abnormality detection problems. One of the most well-known support vector learning methods for abnormality detection is the so-called SVDD(support vector data description), which seeks the strategy of utilizing balls defined on the kernel feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to modify the SVDD into the direction of utilizing the relation between the optimal solution and incrementally given training data. After a thorough review about the original SVDD method, this paper establishes an incremental method for finding the optimal solution based on certain observations on the Lagrange dual problems. The applicability of the presented incremental method is illustrated via a design example.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.3
• /
• pp.513-522
• /
• 2014

Using the assumption that the price of a stock follows a geometric Brownian motion with constant volatility, Black and Scholes (BS) derived a formula that gives the price of a European call option on the stock as a function of the stock price, the strike price, the time to maturity, the risk-free interest rate, the dividend rate paid by the stock, and the volatility of the stock's return. However, implied volatilities of BS method tend to depend on the stock prices and the time to maturity in practice. To address this shortcoming, we estimate the implied volatility function as a function of the strike priceand the time to maturity for data consisting of the daily prices for KOSPI200 call options from January 2007 to May 2009 using support vector regression (SVR), the multiple additive regression trees (MART) algorithm, and ordinary least squaress (OLS) regression. In conclusion, use of MART or SVR in the BS pricing model reduced both RMSE and MAE, compared to the OLS-based BS pricing model.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.3
• /
• pp.297-305
• /
• 2008

Recently, kernel methods have attracted great interests in the areas of pattern classification, function approximation, and anomaly detection. The role of the kernel is particularly important in the methods such as SVM(support vector machine) and KPCA(kernel principal component analysis), for it can generalize the conventional linear machines to be capable of efficiently handling nonlinearities. This paper considers the problem of combining radar and rain gauge observations utilizing the regression approach based on the kernel-based gaussian process and support vector learning. The data-assimilation results of the considered methods are reported for the radar and rain gauge observations collected over the region covering parts of Gangwon, Kyungbuk, and Chungbuk provinces of Korea, along with performance comparison.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.2
• /
• pp.321-328
• /
• 2019

A military shelter for communication and information is necessary to optimize the integrated combat ability of weapon systems in the network centric warfare. Therefore, the military shelter is required for EMI shielding performance. This study examines the stable measurement points for EMI shielding effectiveness of a military shelter for communication and information. The measurement points were found by analyzing the EMI shielding effectiveness measurement data with data mining technique and process capability analysis. First, a support vector machine was used to separate the measurement point that has stable EMI shielding effectiveness according to set condition. Second, this process was conducted with process capability analysis. Finally, the results of data mining technique were compared with those of process capability analysis. As a result, 24 measurement points with stable EMI shielding effectiveness were found.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.25-25
• /
• 2022

하천의 개발 및 보전 계획을 수립하는 데에 있어 자연하천의 부유사량 및 총유사량을 계측하는 것은 매우 중요하다. 우리나라에서는 매년 국내 자연하천을 대상으로 부유사량을 실측하고 실측 부유사량을 바탕으로 수정 아인슈타인 방법을 적용해 총유사량을 산정하고 있으나 이 또한 홍수기에 국한되어 있다. 가장 일반적인 유사량 계측 방법인 시료 채집에 의한 방법은 많은 노력과 비용을 수반하기 때문에 유사량 관측소와 관측 빈도를 늘릴 수 없는 실정이다. 최근에는 ADCP 음파 신호의 후방산란도가 부유사 농도에 따라 증가한다는 성질을 이용해 부유사 농도 계측에 ADCP를 이용하고자 하는 노력이 계속되고 있다. 이러한 특성을 이용해 본 연구에서는 전라남도 나주시에 위치한 남평교 자동유량관측소에 설치된 횡방향 ADCP (H-ADCP)를 대상으로 서포트 벡터 회귀(SVR)를 적용한 실시간 유사량 모니터링 모형을 제안하였다. 여기서 제시하는 유사량산정 모형은 크게 유량과 초음파 산란도를 입력 변수로 해 부유사 농도를 산정하는 서포트 벡터 회귀 모형과 첫 번째 모형으로부터 산정된 부유사 농도와 흐름 정보를 이용해 총유사량을 산정하는 모형으로 구성되어 있다. 개발된 SVR 부유사량 및 총유사량 산정 모형의 정확도가 결정계수(R²) 기준으로 각각 0.82, 0.90 으로 나타났다. 주목할 점은, 본 연구에서 제시하는 SVR 모형을 이용해 멱함수 기반 유사량 관계식으로는 예측할 수 없는 유사량의 이력현상을 재현해낼 수 있다는 것이다. 본 연구에서 제시하는 H-ADCP 기반 총유사량 모니터링 방법은 기존 자동 유량 관측소 시설을 그대로 이용할 수 있다는 장점이 있다. 따라서 실무 적용 시 낮은 추가비용으로 양질의 유사량 모니터링이 가능할 것으로 기대된다.

• Journal of the Korean Data and Information Science Society
• /
• v.20 no.6
• /
• pp.1093-1101
• /
• 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.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.4
• /
• pp.783-791
• /
• 2013

There are sixteen indicators of "College Information" found on the website of College Information Disclosure Center. Among these indicators, the current study examined an enrollment rate and an employment rate based on health insurance coverage, and focused on twenty-four universities in Daegu and Gyeongbuk area. The universities were classified into groups by the enrollment rate and employment rate. This study investigated the characteristics pertaining to those different groups. Hierarchical cluster analysis and support vector cluster analysis were conducted in order to analyze the characteristics of the groups statistically.