Changha Hwang

发表

Transcription factor-binding site identification and gene classification via fusion of the supervised-weighted discrete kernel clustering and support vector machine

Insuk Sohn, Jooyong Shim, Changha Hwang, 2014 .

Analyzing the Nonlinear Time Series of Turbulent Flows with Kernel Interval Regression Machine

Dug Hun Hong, Changha Hwang, D. Hong, 2005, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

Varying coefficient modeling via least squares support vector regression

Jooyong Shim, Changha Hwang, Changha Hwang, 2015, Neurocomputing.

Interval regression analysis using quadratic loss support vector machine

Dug Hun Hong, Changha Hwang, D. Hong, 2005, IEEE Transactions on Fuzzy Systems.

Semiparametric mixed-effect least squares support vector machine for analyzing pharmacokinetic and pharmacodynamic data

Jooyong Shim, Changha Hwang, Kyung Ha Seok, 2011, Neurocomputing.

T-sum of sigmoid-shaped fuzzy intervals

Dug Hun Hong, Changha Hwang, Kyung Tae Kim, 2007, Inf. Sci..

Estimating value at risk with semiparametric support vector quantile regression

Jooyong Shim, Changha Hwang, Yong-Tae Kim, 2012, Comput. Stat..

Doubly Regularized Kernel Regression with Heteroscedastic Censored Data

Jooyong Shim, Changha Hwang, 2005, ICNC.

A Simple Quantile Regression via Support Vector Machine

Jooyong Shim, Changha Hwang, Changha Hwang, 2005, ICNC.

Exon Structure Analysis via PCA and ICA of Short-Time Fourier Transform

Insuk Sohn, Changha Hwang, David K. Y. Chiu, 2005, ICNC.

Extended fuzzy regression models using regularization method

Dug Hun Hong, Changha Hwang, D. Hong, 2004, Inf. Sci..

Kernel Adatron Algorithm for Supprot Vector Regression

Changha Hwang, Kyung-Ha Seok, 1999 .

Support vector fuzzy regression machines

Dug Hun Hong, Changha Hwang, D. Hong, 2003, Fuzzy Sets Syst..

Support vector interval regression machine for crisp input and output data

Dug Hun Hong, Changha Hwang, Kyung Ha Seok, 2006, Fuzzy Sets Syst..

On Approximate Prediction Intervals for Support Vector Machine Regression

Changha Hwang, Daehyeon Cho, Kyung-Ha Seok, 2002 .

Predicting Bitcoin Market Trend with Deep Learning Models

Changha Hwang, Seo Yunbeom, Changha Hwang, 2018 .

A Prediction Interval Estimation Method for KMSE

Changha Hwang, Kyung Ha Seok, Daehyeon Cho, 2005, ICNC.

Non-crossing quantile regression via doubly penalized kernel machine

Changha Hwang, Kyung Ha Seok, Joo-Yong Shim, 2009, Comput. Stat..

Composite support vector quantile regression estimation

Jooyong Shim, Changha Hwang, Kyung-Ha Seok, 2014, Comput. Stat..

Semiparametric spatial effects kernel minimum squared error model for predicting housing sales prices

Jooyong Shim, Changha Hwang, Okmyung Bin, 2014, Neurocomputing.

Software Reliability Assessment with Fuzzy Least Squares Support Vector Machine Regression

Dug Hun Hong, Changha Hwang, Jang Han Kim, 2003 .

Multioutput LS-SVR based residual MCUSUM control chart for autocorrelated process

Changha Hwang, Changha Hwang, 2016 .

New normalization methods using support vector machine quantile regression approach in microarray analysis

Insuk Sohn, Changha Hwang, Sujong Kim, 2008, Comput. Stat. Data Anal..

AndroClass: An Effective Method to Classify Android Applications by Applying Deep Neural Networks to Comprehensive Features

Masoud Reyhani Hamedani, Seong-je Cho, Changha Hwang, 2018, Wirel. Commun. Mob. Comput..

Estimating small area mean with mixed and fixed effects support vector median regressions

Jooyong Shim, Changha Hwang, Changha Hwang, 2014, Neurocomputing.

Support vector quantile regression with varying coefficients

Jooyong Shim, Changha Hwang, Kyung-Ha Seok, 2016, Comput. Stat..

A Study on Automatic Learning of Weight Decay Neural Network

Changha Hwang, Kyung-Ha Seok, Eun-Young Na, 2001 .

Kernel-based orthogonal quantile regression model

Jooyong Shim, Changha Hwang, Changha Hwang, 2017, Model. Assist. Stat. Appl..

Extended Fuzzy Regression Models

Dug Hun Hong, Changha Hwang, 2002, JCIS.

Ridge estimation for regression models with crisp inputs and Gaussian fuzzy output

Dug Hun Hong, Changha Hwang, Chulhwan Ahn, 2004, Fuzzy Sets Syst..

Selecting marker genes for cancer classification using supervised weighted kernel clustering and the support vector machine

Insuk Sohn, Jooyong Shim, Changha Hwang, 2009, Comput. Stat. Data Anal..

Combination of Value-at-Risk Models with Support Vector Machine

Changha Hwang, Jang-Taek Lee, Joo-Yong Shim, 2009 .

Bayesian Model Selection for Support Vector Regression using the Evidence Framework

Changha Hwang, Kyung-Ha Seok, 1999 .

Multiclass Classification via Least Squares Support Vector Machine Regression

Changha Hwang, Joo-Yong Shim, Jong-Sig Bae, 2008 .

A T-sum bound of LR-fuzzy numbers

Dug Hun Hong, Changha Hwang, D. Hong, 1997, Fuzzy Sets Syst..

Locally Weighted LS-SVM for Fuzzy Nonlinear Regression with Fuzzy Input-Output

Dug Hun Hong, Jooyong Shim, Changha Hwang, 2006, 2006 International Conference on Computational Intelligence and Security.

Interval Regression Analysis Using Support Vector Machine and Quantile Regression

Dug Hun Hong, Jooyong Shim, Changha Hwang, 2005, FSKD.

Kernel-based random effect time-varying coefficient model for longitudinal data

Insuk Sohn, Jooyong Shim, Changha Hwang, 2017, Neurocomputing.

Fuzzy Nonlinear Regression Model Based on LS-SVM in Feature Space

Dug Hun Hong, Changha Hwang, 2006, FSKD.

Support vector censored quantile regression under random censoring

Jooyong Shim, Changha Hwang, Changha Hwang, 2009, Comput. Stat. Data Anal..

Informative transcription factor selection using support vector machine-based generalized approximate cross validation criteria

Insuk Sohn, Jooyong Shim, Changha Hwang, 2009, Comput. Stat. Data Anal..

Incremental Feature Extraction Based on Empirical Kernel Map

Byung-Joo Kim, Jooyong Shim, Changha Hwang, 2003, ISMIS.

Rating Information-Aided Denoising AutoEncoder for Effective Collaborative Filtering

Changha Hwang, Won-Yong Shin, Dongjin Shin, 2018 .

Robust LS-SVM Regression Using Fuzzy C-Means Clustering

Jooyong Shim, Changha Hwang, Sungkyun Nau, 2006, ICNC.

PREDICTION INTERVALS FOR SUPPORT VECTOR MACHINE REGRESSION

Changha Hwang, Daehyeon Cho, Kyung-Ha Seok, 2002 .

Feature selection in the semivarying coefficient LS-SVR

Jooyong Shim, Changha Hwang, Changha Hwang, 2017 .

Generalized kernel estimating equation for panel estimation of small area unemployment rates

Jooyong Shim, Changha Hwang, Changha Hwang, 2013 .

Prediction Intervals for LS-SVM Regression using the Bootstrap

Changha Hwang, Joo-Yong Shim, 2003 .

Detection of chromosome structural variation by targeted next-generation sequencing and a deep learning application

Insuk Sohn, Jooyong Shim, Changha Hwang, 2019, Scientific Reports.

Support Vector Quantile Regression with Weighted Quadratic Loss Function

Changha Hwang, Joo-Yong Shim, 2010 .

Semiparametric least squares support vector machine for accelerated failure time model

Changha Hwang, Choongrak Kim, Joo-Yong Shim, 2011 .

Semivarying coefficient least-squares support vector regression for analyzing high-dimensional gene-environmental data

Insuk Sohn, Jooyong Shim, Changha Hwang, 2018 .