Search Results - Huber regression and support vector regression

Large-scale robust regression with truncated loss via majorization-minimization algorithm by Huang, Ling-Wei, Shao, Yuan-Hai, Lv, Xiao-Jing, Li, Chun-Na

“…The utilization of regression methods employing truncated loss functions is widely praised for its robustness in handling outliers and representing the solution in the sparse form of the samples…”
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On Regularization Based Twin Support Vector Regression with Huber Loss: On Regularization Based Twin Support Vector Regression with by Gupta, Umesh, Gupta, Deepak

“…Twin support vector regression (TSVR) is generally employed with ε -insensitive loss function which is not well capable to handle the noises and outliers…”
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On pairing Huber support vector regression by Balasundaram, S., Prasad, Subhash Chandra

“…In this paper, a novel and efficient pairing support vector regression learning method using ε…”
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Robust control chart for nonlinear conditionally heteroscedastic time series based on Huber support vector regression by Kim, Chang Kyeom, Yoon, Min Hyeok, Lee, Sangyeol

“…This study proposes a control chart that monitors conditionally heteroscedastic time series by integrating the Huber support vector regression (HSVR…”
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Incremental Huber-Support vector regression based online robust parameter design by Zhou, Xiaojian, Xiao, Dan, Yu, Jieyao, Jiang, Ting

“…In the response surface based RPD, the optimal setting of the controllable factor is highly dependent on the accuracy of the response surface. Classically, in…”
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On Regularization Based Twin Support Vector Regression with Huber Loss by Gupta, Umesh, Gupta, Deepak

“…Twin support vector regression (TSVR) is generally employed with -insensitive loss function which is not well capable to handle the noises and outliers…”
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Online monitoring for volatility change of time series via asymmetric Huber support vector regression by Lee, Sangyeol, Yoon, Min Hyeok, Kim, Chang Kyeom

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Robust twin support vector regression based on Huber loss function by Balasundaram, S., Prasad, Subhash Chandra

“… Since Huber function has the property that inputs with large deviations of misfit are penalized linearly and small errors are squared, we present novel robust regularized twin support vector machines…”
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Robust Support Vector Regression in Primal with Asymmetric Huber Loss by Balasundaram, S., Meena, Yogendra

“… In this work, we propose novel robust regularized support vector regression models with asymmetric Huber and ε…”
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A robust algorithm of support vector regression with a trimmed Huber loss function in the primal by Chen, Chuanfa, Yan, Changqing, Zhao, Na, Guo, Bin, Liu, Guolin

“…Support vector machine for regression (SVR) is an efficient tool for solving function estimation problem…”
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Robust support vector regression with generic quadratic nonconvex ε-insensitive loss by Ye, Yafen, Gao, Junbin, Shao, Yuanhai, Li, Chunna, Jin, Yan, Hua, Xiangyu

“…In this paper, we propose a robust support vector regression with a novel generic nonconvex quadratic ε…”
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Huber SVR-based Hybrid Models for Significant Wave Height Forecasting using Buoy Sensors by Anand, Pritam, Jain, Shantanu, Mishra, Rahul

“…). However, accurate forecasting of SWH is still challenging in energy production. To circumvent such a challenge, this work introduces a Huber Support Vector Regression (SVR…”
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Structure-Activity Relationships in Chromatography: Retention Prediction of Oligonucleotides with Support Vector Regression by Kohlbacher, Oliver, Quinten, Sascha, Sturm, Marc, Mayr, Bettina M., Huber, Christian G.

“… With the aid of machine learning through support vector regression, the retention of oligonucleotides was modeled in ion‐pair reversed…”
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Prediction of Biomechanical Properties of Trabecular Bone in MR Images With Geometric Features and Support Vector Regression by Huber, Markus B., Lancianese, Sarah L., Nagarajan, Mahesh B., Ikpot, Imoh Z., Lerner, Amy L., Wismuller, Axel

“…Whole knee joint MR image datasets were used to compare the performance of geometric trabecular bone features and advanced machine learning techniques in…”
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Improving bone strength prediction in human proximal femur specimens through geometrical characterization of trabecular bone microarchitecture and support vector regression by Yang, Chien-Chun, Nagarajan, Mahesh B, Huber, Markus B, Carballido-Gamio, Julio, Bauer, Jan S, Baum, Thomas, Eckstein, Felix, Lochmüller, Eva, Majumdar, Sharmila, Link, Thomas M, Wismüller, Axel

“…We investigate the use of different trabecular bone descriptors and advanced machine learning tech niques to complement standard bone mineral density (BMD)…”
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Computational investigations on anti-cancer drug solubility in supercritical solvent for efficient cancer therapy by Wang, Ziming, Wan, Yu, Li, Hongxia, Wang, Yi

“…) regression, Huber regression, and Support Vector Machine (SVM) regression—were employed to predict solubility, with hyper-parameter tuning accomplished using the Cuckoo Search (CS…”
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Automated support vector regression by Harrington, Peter de B.

“… Support vector regression (SVR) is a powerful method for accommodating megavariate data…”
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An improved regularization based Lagrangian asymmetric ν-twin support vector regression using pinball loss function by Gupta, Umesh, Gupta, Deepak

“…In twin support vector regression (TSVR), one can notice that the samples are having the same importance even they are laying above the up-bound and below the down-bound on the estimation function for regression problem…”
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Predicting the Biomechanical Strength of Proximal Femur Specimens through High Dimensional Geometric Features and Support Vector Regression by Yang, Chien-Chun, Nagarajan, Mahesh B, Huber, Markus B, Carballido-Gamio, Julio, Bauer, Jan S, Baum, Thomas, Eckstein, Felix, Lochmüller, Eva, Majumdar, Sharmila, Link, Thomas M, Wismüller, Axel

“…Estimating local trabecular bone quality for purposes of femoral bone strength prediction is important for improving the clinical assessment of osteoporotic…”
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Robust monitoring conditional volatility change for time series based on support vector regression by Yoon, Min Hyeok, Kim, Chang Kyeom, Lee, Sangyeol

“…) method hybridized with the asymmetric Huber support vector regression, named AHSVR. AHSVR-GARCH model provides an effective way to model nonlinear GARCH time series…”
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