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Changha Hwang,Jooyong Shim 한국데이터정보과학회 2016 한국데이터정보과학회지 Vol.27 No.3
In this paper, we propose a deep least squares support vector machine (LS-SVM) for regression problems, which consists of the input layer and the hidden layer. In the hidden layer, LS-SVMs are trained with the original input variables and the perturbed responses. For the final output, the main LS-SVM is trained with the outputs from LS-SVMs of the hidden layer as input variables and the original responses. In contrast to the multilayer neural network (MNN), LS-SVMs in the deep LS-SVM are trained to minimize the penalized objective function. Thus, the learning dynamics of the deep LS-SVM are entirely different from MNN in which all weights and biases are trained to minimize one final error function. When compared to MNN approaches, the deep LS-SVM does not make use of any combination weights, but trains all LS-SVMs in the architecture. Experimental results from real datasets illustrate that the deep LS- SVM significantly outperforms state of the art machine learning methods on regression problems.
Geographically weighted least squares-support vector machine
Hwang, Changha,Shim, Jooyong The Korean Data and Information Science Society 2017 한국데이터정보과학회지 Vol.28 No.1
When the spatial information of each location is given specifically as coordinates it is popular to use the geographically weighted regression to incorporate the spatial information by assuming that the regression parameters vary spatially across locations. In this paper, we relax the linearity assumption of geographically weighted regression and propose a geographically weighted least squares-support vector machine for estimating geographically weighted mean by using the basic concept of kernel machines. Generalized cross validation function is induced for the model selection. Numerical studies with real datasets have been conducted to compare the performance of proposed method with other methods for predicting geographically weighted mean.
Hwang, Changha,Kim, Daehak The Korean Statistical Society 1997 Communications for statistical applications and me Vol.4 No.3
Statistical relations between a system and empirical distribution are studied in terms of the concept of Akaike's Information Criterion. From this consideration we derive a bootstrap criterion for determining the optimal number of hidden units in nerual networks.
Multioutput LS-SVR based residual MCUSUM control chart for autocorrelated process
Hwang, Changha The Korean Data and Information Science Society 2016 한국데이터정보과학회지 Vol.27 No.2
Most classical control charts assume that processes are serially independent, and autocorrelation among variables makes them unreliable. To address this issue, a variety of statistical approaches has been employed to estimate the serial structure of the process. In this paper, we propose a multioutput least squares support vector regression and apply it to construct a residual multivariate cumulative sum control chart for detecting changes in the process mean vector. Numerical studies demonstrate that the proposed multioutput least squares support vector regression based control chart provides more satisfying results in detecting small shifts in the process mean vector.
Support Vector Machine for Linear Regression
Hwang, Changha,Seok, Kyungha The Korean Statistical Society 1999 Communications for statistical applications and me Vol.6 No.2
Support vector machine(SVM) is a new and very promising regression and classification technique developed by Vapnik and his group at AT&T Bell laboratories. This article provides a brief overview of SVM focusing on linear regression. We explain from statistical point of view why SVM might be attractive and how this could be compared with other linear regression techniques. Furthermore. we explain model selection based on VC-theory.
Geographically weighted least squares-support vector machine
Changha Hwang,Jooyong Shim 한국데이터정보과학회 2017 한국데이터정보과학회지 Vol.28 No.1
When the spatial information of each location is given specifically as coordinates it is popular to use the geographically weighted regression to incorporate the spatial information by assuming that the regression parameters vary spatially across locations. In this paper, we relax the linearity assumption of geographically weighted regression and propose a geographically weighted least squares-support vector machine for estimating geographically weighted mean by using the basic concept of kernel machines. Generalized cross validation function is induced for the model selection. Numerical studies with real datasets have been conducted to compare the performance of proposed method with other methods for predicting geographically weighted mean.
Multioutput LS-SVR based residual MCUSUM control chart for autocorrelated process
Changha Hwang 한국데이터정보과학회 2016 한국데이터정보과학회지 Vol.27 No.2
Most classical control charts assume that processes are serially independent, and autocorrelation among variables makes them unreliable. To address this issue, a variety of statistical approaches has been employed to estimate the serial structure of the process. In this paper, we propose a multioutput least squares support vector regression and apply it to construct a residual multivariate cumulative sum control chart for detecting changes in the process mean vector. Numerical studies demonstrate that the proposed multioutput least squares support vector regression based control chart provides more satisfying results in detecting small shifts in the process mean vector.
Feature selection in the semivarying coefficient LS-SVR
Changha Hwang,Jooyoung Shim 한국데이터정보과학회 2017 한국데이터정보과학회지 Vol.28 No.2
In this paper we propose a feature selection method identifying important features in the semivarying coefficient model. One important issue in semivarying coefficient model is how to estimate the parametric and nonparametric components. Another issue is how to identify important features in the varying and the constant effects. We propose a feature selection method able to address this issue using generalized cross validation functions of the varying coefficient least squares support vector regression (LS-SVR) and the linear LS-SVR. Numerical studies indicate that the proposed method is quite effective in identifying important features in the varying and the constant effects in the semivarying coefficient model.
Subject Specific Deep Neural Network for Longitudinal Study in Pharmacokinetics and Pharmacodynamics
Changha Hwang 계명대학교 자연과학연구소 2022 Quantitative Bio-Science Vol.41 No.2
In this study we propose a subject specific deep neural network (SSDNN) model for analyzing pharmacokinetic (PK) and pharmacodynamic (PD) data. PK and PD data are obtained at subject-specific irregular time intervals, and a different number of observations are collected for each subject, based on the number of times the subject visited the hospital. The SSDNN’s performance is compared to that of the standard neural network (NN) and support vector machine (SVM) using three evaluation metrics, which are mean squared error (MSE), mean absolute error (MAE) and mean relative absolute error (MRAE). We find that the absolute values of the four measures of the proposed SSDNN are significantly lower than those of NN and SVR for PK and PD data. These findings imply that the proposed SSDNN is an appealing tool for analyzing PK and PD data.