• 대한수학회지
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• 제31권1호
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• pp.139-148
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• 1994

• Kyungpook Mathematical Journal
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• 제18권1호
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• pp.119-123
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• 1978

• 한국수학교육학회지시리즈A:수학교육
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• 제14권1호
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• pp.13-16
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• 1975

• 한국멀티미디어학회논문지
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• 제11권7호
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• pp.1035-1042
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• 2008

이진 분류기로서 지지벡터기계는 다양한 응용을 통해 이진 분류 문제에서 기존의 패턴 분류기들보다 우수한 성능을 보였다. 지지벡터기계의 바탕이 되는 최대 마진 분류 이론을 다중 분류 문제에 확장은 어려움이 있다. 이 논문에서는 다중 분류 문제를 위한 지지벡터기계의 학습 전략을 논의하였으며 성능 비교를 수행하였다. 학습 데이터의 분배 전략에 따라 지지벡터기계는 고유의 이진 분류 특징을 수정하지 않고 다중분류 문제에 쉴게 적용될 수 있다. 다양한 벤치마킹 데이터에 대해 선택된 학습 전략, 커널함수, 학습 소요시간 등에 따라 성능비교가 수행되었고 오류역전파 학습의 신경망의 테스트 결과와 비교되었다. 신경망 모델과 비교 실험에서 지지벡터기계는 일반적인 다중 분류 문제에 응용성과 효과가 있음을 보였다.

• Journal of applied mathematics & informatics
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• 제6권2호
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• pp.523-538
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• 1999

A new preconditioning method is investigated to reduce the roundoff error in computing derivatives using Chebyshev col-location methods(CCM). Using this preconditioning causes ration of roundoff error of preconditioning method and CCm becomes small when N gets large. Also for accuracy enhancement of differentiation we use a domain decomposition approach. Error analysis shows that for this domain decomposition method error reduces proportional to the length of subintervals. Numerical results show that using domain decomposition and preconditioning simultaneously gives super accu-rate approximate values for first derivative of the function and good approximate values for moderately high derivatives.

• 인지과학
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• 제23권3호
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• pp.295-322
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• 2012

본 연구에서는 벡터 공간 모델과 HAL (Hyperspace Analog to Language)을 적용해서 단어 의미 유사성을 군집한다. 일정한 크기의 문맥을 통해서 단어 간의 상관성을 측정하는 HAL을 도입하고(Lund and Burgess 1996), 상관성 측정에서 고빈도와 저빈도에 다르게 측정되는 왜곡을 줄이기 위해서 벡터 공간 모델을 적용해서 단어 쌍의 코사인 유사도를 측정하였다(Salton et al. 1975, Widdows 2004). HAL과 벡터 공간 모델로 만들어지는 공간은 다차원이므로, 차원을 축소하기 위해서 PCA (Principal Component Analysis)와 SVD (Singular Value Decomposition)를 적용하였다. 유사성 군집을 위해서 비감독 방식과 감독 방식을 적용하였는데, 비감독 방식에는 클러스터링을 감독 방식에는 SVM (Support Vector Machine), 나이브 베이즈 구분자(Naive Bayes Classifier), 최대 엔트로피(Maximum Entropy) 방식을 적용하였다. 이 연구는 언어학적 측면에서 Harris (1954), Firth (1957)의 분포 가설(Distributional Hypothesis)을 활용한 의미 유사도를 측정하였으며, 심리언어학적 측면에서 의미 기억을 설명하기 위한 모델로 벡터 공간 모델과 HAL을 결합하였으며, 전산적 언어 처리 관점에서 기계학습 방식 중 감독 기반과 비감독 기반을 적용하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.136-136
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• 2022

This study investigates the possibility of coupling empirical mode decomposition (EMD) for runoff prediction from machine learning (ML) models. Here, support vector regression (SVR) and convolutional neural network (CNN) were considered for ML algorithms. Precipitation (P), minimum temperature (Tmin), maximum temperature (Tmax) and their intrinsic mode functions (IMF) values were used for input variables at a monthly scale from Jan. 1973 to Dec. 2020 in the Grand river basin, Canada. The support vector machine-recursive feature elimination (SVM-RFE) technique was applied for finding the best combination of predictors among input variables. The results show that the proposed method outperformed the individual performance of SVR and CNN during the training and testing periods in the study area. According to the correlation coefficient (R), the EMD-SVR model outperformed the EMD-CNN model in both training and testing even though the CNN indicated a better performance than the SVR before using IMF values. The EMD-SVR model showed higher improvement in R value (38.7%) than that from the EMD-CNN model (7.1%). It should be noted that the coupled models of EMD-SVR and EMD-CNN represented much higher accuracy in runoff prediction with respect to the considered evaluation indicators, including root mean square error (RMSE) and R values.

• 응용통계연구
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• 제24권6호
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• pp.995-1006
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• 2011

A time series can be decomposed into simple components with a multiscale method. Empirical mode decomposition(EMD) is a recently invented multiscale method in Huang et al. (1998). It is natural to apply a classical prediction method such a vector autoregressive(AR) model to the obtained simple components instead of the original time series; in addition, a prediction procedure combining a classical prediction model to EMD and Hilbert spectrum is proposed in Kim et al. (2008). In this paper, we suggest to adopt principal component analysis(PCA) to the prediction procedure that enables the efficient selection of input variables among obtained components by EMD. We discuss the utility of adopting PCA in the prediction procedure based on EMD and Hilbert spectrum and analyze the daily worm account data by the proposed PCA adopted prediction method.

• 한국경영과학회지
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• 제28권1호
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• pp.11-23
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• 2003

This study Investigates the short and long-run dynamic relationships between the domestic and U.S. stock markets for the period of declining stock prices. It Is well known that the domestic stock market variations are largely caused by the U.S. stock market movements. Multivariate causal tty test Is utilized to examine the lead-lag relationships among four stock prices of KOSPI and KOSDAQ In the domestic part and DOWJONES and NASDAQ In the U.S. part. When the stock prices tend to decrease In the long run, It Is found that both KOSPI and KOSDAQ have closer relations with NASDAQ than DOWJONES. When both of domestic stock markets are severely fluctuate, bidirectional causal relationships appear to exist between NASDAQ and each of KOSPI and KOSDAQ. On the other hand. when the domestic stock markets are relatively stable, unidirectional causality Is found to exist between NASDAQ and each of KOSPI and KOSDAQ. which is explicitly validated by the analysis of variance decomposition.

• 경영과정보연구
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• 제27권
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• pp.131-147
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• 2008

The study examines common component existing in five Asian countries from 1991 to 2007. To do this, the daily stock market indices of Korea, Malaysia, Thailand, Indonesia, and the Philippines were used. Using a Vector Autoregressive Model this paper analyzes causal relations and dynamic interactions between five Asian stock markets. The findings in this study indicate that level of five Asian stock markets' stock return linkages are low. First, from the statistics for pair-wise Granger causality tests, I find Granger-causal relationship between Korea and Indonesia and between Malaysia and and Indonesia. Second, from the results of response function and the statistics of variance decomposition, I find that week shocks to Korean stock market return on Malaysia, Indonesia, Thailand, and the Philippines stock market returns. The results indicate increased Asian stock market linkages but the level is very low. This implies that the benefits of diversification within the five Asian stock markets are still existed.