• 응용통계연구
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• 제21권1호
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• pp.159-168
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• 2008

수익률 기간구조(term structure of interest rates, 이하 수익률곡선)는 자료의 성격이 경시적(longitudinal)이므로 만기까지 기간과 시간을 동시에 입력변수로 고려해야만 유용하고 효율적인 함수추정이 가능하다. 고러나 이러한 방법은 다루어야 하는 자료가 대용량이기 때문에 대용량 자료에 적합하고 실행속도가 빠른 추정기법을 개발하는 것이 필요하다. 한편 자료에 내재하는 자기상관성 구조 때문에 과대 적합된 추정 결과를 얻기 쉽다. 따라서 본 논문에서는 이러한 문제를 해결하기 위해서 가중 LS-SVM(least squares support vector machine, 최소제곱 서포트벡터기계)의 혼합모형을 제안한다. 미국 재무부 채권에 대한 사례연구를 통해서 추정 결과가 증권시장 붕괴 같은 이례적 사건의 현상을 잘 반영하고 있음을 확인할 수 있었다.

• Journal of the Korean Data and Information Science Society
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• 제14권4호
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• pp.965-974
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• 2003

In this paper we propose an incremental classification of multi-class data set by LS-SVM. By encoding the output variable in the training data set appropriately, we obtain a new specific output vectors for the training data sets. Then, online LS-SVM is applied on each newly encoded output vectors. Proposed method will enable the computation cost to be reduced and the training to be performed incrementally. With the incremental formulation of an inverse matrix, the current information and new input data are used for building another new inverse matrix for the estimation of the optimal bias and lagrange multipliers. Computational difficulties of large scale matrix inversion can be avoided. Performance of proposed method are shown via numerical studies and compared with artificial neural network.

• Geomechanics and Engineering
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• 제11권3호
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• pp.361-372
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• 2016

The soil-concrete interface shear strength, although has been extensively studied, is still difficult to predict as a result of the dependence on many factors such as normal stresses, surface roughness, particle sizes, moisture contents, dilation angles of soils, etc. In this study, a well-known rigorous statistical learning approach, namely the least squares support vector machine (LS-SVM) realized in a ubiquitous spreadsheet platform is firstly used in estimating the soil-structure interface shear strength. Instead of studying the complicated mechanism, LS-SVM enables to explore the possible link between the fundamental factors and the interface shear strengths, via a sophisticated statistic approach. As a preliminary investigation, the authors study the expansive soils that are found extensively in most countries. To reduce the complexity, three major influential factors, e.g., initial moisture contents, initial dry densities and normal stresses of soils are taken into account in developing the LS-SVM models for the soil-concrete interface shear strengths. The predicted results by LS-SVM show reasonably good agreement with experimental data from direct shear tests.

• Journal of Information Processing Systems
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• 제1권1호
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• pp.9-13
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• 2005

Computer security has become a critical issue with the rapid development of business and other transaction systems over the Internet. The application of artificial intelligence, machine learning and data mining techniques to intrusion detection systems has been increasing recently. But most research is focused on improving the classification performance of a classifier. Selecting important features from input data leads to simplification of the problem, and faster and more accurate detection rates. Thus selecting important features is an important issue in intrusion detection. Another issue in intrusion detection is that most of the intrusion detection systems are performed by off-line and it is not a suitable method for a real-time intrusion detection system. In this paper, we develop the real-time intrusion detection system, which combines an on-line feature extraction method with the Least Squares Support Vector Machine classifier. Applying the proposed system to KDD CUP 99 data, experimental results show that it has a remarkable feature extraction and classification performance compared to existing off-line intrusion detection systems.

• Journal of Computing Science and Engineering
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• 제9권2호
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• pp.39-50
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• 2015

Genetic algorithm (GA) and particle swarm optimization (PSO) are two excellent approaches to multimodal optimization problems. However, slow convergence or premature convergence readily occurs because of inappropriate and inflexible evolution. In this paper, a novel optimization algorithm with a flexible grid optimization (FGO) is suggested to provide adaptive trade-off between exploration and exploitation according to the specific objective function. Meanwhile, a uniform agents array with adaptive scale is distributed on the gird to speed up the calculation. In addition, a dominance centroid and a fitness center are proposed to efficiently determine the potential guides when the population size varies dynamically. Two types of subregion division strategies are designed to enhance evolutionary diversity and convergence, respectively. By examining the performance on four benchmark functions, FGO is found to be competitive with or even superior to several other popular algorithms in terms of both effectiveness and efficiency, tending to reach the global optimum earlier. Moreover, FGO is evaluated by applying it to a parameter decision in a least squares support vector machine (LS-SVM) to verify its practical competence.

• 정보과학회 논문지
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• 제42권12호
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• pp.1535-1543
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• 2015

뇌-컴퓨터 인터페이스는 사용자의 뇌전도(Electroencephalogram: EEG)를 획득하여 생각만으로 기계를 제어하거나 신체장애를 가진 사람에게 손 또는 발과 같은 신체를 대신하여 의사 전달 수단으로 사용될 수 있다. 본 논문에서는 동작 상상 EEG를 분류하기 위해 Sub-Band Common Spatial Pattern(SBCSP)를 기반으로 필터 선택을 하지 않는 특징 추출 방법에 대해 연구한다. 4~40Hz의 동작 상상 신호를 4Hz 대역마다 나눈 9개의 서브 밴드에 각각 CSP를 적용한다. 이후 Fisher's Linear Discriminant(FLD)를 사용하여 도출된 값들을 결합한 FLD 점수 벡터에 차원 축소를 위한 Principal Component Analysis(PCA)를 적용하여 클래스 구분을 위한 최적의 평면에 특징을 투영한다. 데이터베이스는 BCI CompetitionIII dataset IVa(2 클래스: 오른손 다리)를 이용하며, 추출된 특징은 Least Squares Support Vector Machine(LS-SVM)의 입력으로 사용된다. 제안된 방법의 성능은 $10{\times}10$ fold cross-validation을 이용하여 분류 정확도로 나타낸다. 본 논문에서 제안하는 방법은 피험자 'aa', 'al', 'av', 'aw', 'ay'에 대하여 각각 $85.29{\pm}0.93%$, $95.43{\pm}0.57%$, $72.57{\pm}2.37%$, $91.82{\pm}1.38%$, $93.50{\pm}0.69%$의 분류 정확도를 보였다.

• Current Optics and Photonics
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• 제4권1호
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• pp.31-43
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• 2020

The feasibility of the application of terahertz electromagnetic waves in the diagnosis of prostate cancer was examined. Four samples of incomplete cancerous prostatic paraffin-embedded tissues were examined using terahertz spectral imaging (TPI) system and the results obtained by comparing the absorption coefficient and refractive index of prostate tumor, normal prostate tissue and smooth muscle from one of the paraffin tissue masses examined were reported. Three hundred and sixty cases of absorption coefficients from one of the paraffin tissues examined were used as raw data to classify these three tissues using the Principal Component Analysis (PCA) and Least Squares Support Vector Machine (LS-SVM). An excellent classification with an accuracy of 92.22% in the prediction set was achieved. Using the distribution information of THz reflection signal intensity from sample surface and absorption coefficient of the sample, an attempt was made to use the TPI system to identify the boundaries of the different tissues involved (prostate tumors, normal and smooth muscles). The location of three identified regions in the terahertz images (frequency domain slice absorption coefficient imaging, 1.2 THz) were compared with those obtained from the histopathologic examination. The tissue tumor region had a distinctively visible color and could well be distinguished from other tissue regions in terahertz images. Results indicate that a THz spectroscopy imaging system can be efficiently used in conjunction with the proposed advanced computer-based mathematical analysis method to identify tumor regions in the paraffin tissue mass of prostate cancer.