13.3.10.5 Regression

Chapter Contents (Back)
Regression.

Yun, W.H.[Woo-Han], Bang, S.Y.[Sung Yang], Kim, D.J.[Dai-Jin],
Real-time Object Recognition using Relational Dependency based on Graphical Model,
PR(41), No. 2, February 2008, pp. 742-753.
Elsevier DOI 0711
BibRef
Earlier: ICPR06(III: 28-32).
IEEE DOI 0609
Object recognition; Graphical model; Relational dependency; Logistic regression; The cascaded adaboost detector; Transition matrix BibRef

Liu, G.C.[Guang-Can], Lin, Z.C.[Zhou-Chen], Yu, Y.[Yong],
Multi-output regression on the output manifold,
PR(42), No. 11, November 2009, pp. 2737-2743.
Elsevier DOI 0907
Regression analysis; Support vector regression; Continuous structured prediction; Manifold learning BibRef

Simila, T.[Timo], Tikka, J.[Jarkko],
Combined input variable selection and model complexity control for nonlinear regression,
PRL(30), No. 3, 1 February 2009, pp. 231-236.
Elsevier DOI 0804
Regression; Function approximation; MLP; Multilayer perceptron; Input variable selection; Hidden node selection BibRef

Domingues, M.A.O.[Marco A.O.], de Souza, R.M.C.R.[Renata M.C.R.], Cysneiros, F.J.A.[Francisco Jose A.],
A robust method for linear regression of symbolic interval data,
PRL(31), No. 13, 1 October 2010, pp. 1991-1996.
Elsevier DOI 1003
Symbolic interval-valued data; Symbolic data analysis; Symmetrical linear regression; Outliers BibRef

Ranganathan, A.[Ananth], Yang, M.H.[Ming-Hsuan], Ho, J.,
Online Sparse Gaussian Process Regression and Its Applications,
IP(19), No. 2, February 2011, pp. 391-404.
IEEE DOI 1102
BibRef
Earlier: A1, A2, Only:
Online Sparse Matrix Gaussian Process Regression and Vision Applications,
ECCV08(I: 468-482).
Springer DOI 0810
BibRef

Guo, W.W.[Wei-Wei], Kotsia, I.[Irene], Patras, I.[Ioannis],
Tensor Learning for Regression,
IP(21), No. 2, February 2012, pp. 816-827.
IEEE DOI 1201
BibRef

Kotsia, I.[Irene], Guo, W.W.[Wei-Wei], Patras, I.[Ioannis],
Higher rank Support Tensor Machines for visual recognition,
PR(45), No. 12, December 2012, pp. 4192-4203.
Elsevier DOI 1208
BibRef
And:
Higher Rank Support Tensor Machines,
ISVC12(II: 31-40).
Springer DOI 1209
Support Tensor Machines; CANDECOMP/PARAFAC tensor decomposition; Relative Margin Support Vector Machines; Action recognition; Gait recognition BibRef

Garcia, E., Arora, R., Gupta, M.R.,
Optimized Regression for Efficient Function Evaluation,
IP(21), No. 9, September 2012, pp. 4128-4140.
IEEE DOI 1208
BibRef

Liang, Z.Z.[Zhi-Zheng], Li, Y.F.[You-Fu], Xia, S.X.[Shi-Xiong],
Adaptive weighted learning for linear regression problems via Kullback-Leibler divergence,
PR(46), No. 4, April 2013, pp. 1209-1219.
Elsevier DOI 1301
Linear regression; KL divergence; Weighted learning; Alternative optimization; Image classification BibRef

Shen, F., Shen, C.H., van den Hengel, A.J., Tang, Z.,
Approximate Least Trimmed Sum of Squares Fitting and Applications in Image Analysis,
IP(22), No. 5, May 2013, pp. 1836-1847.
IEEE DOI 1303
regression estimation criterion. BibRef

Fletcher, P.T.[P. Thomas],
Geodesic Regression and the Theory of Least Squares on Riemannian Manifolds,
IJCV(105), No. 2, November 2013, pp. 171-185.
Springer DOI 1309
BibRef

Hinkle, J.[Jacob], Fletcher, P.T.[P. Thomas], Joshi, S.[Sarang],
Intrinsic Polynomials for Regression on Riemannian Manifolds,
JMIV(50), No. 1-2, September 2014, pp. 32-52.
Springer DOI 1408
BibRef

Hinkle, J.[Jacob], Muralidharan, P.[Prasanna], Fletcher, P.T.[P. Thomas], Joshi, S.[Sarang],
Polynomial Regression on Riemannian Manifolds,
ECCV12(III: 1-14).
Springer DOI 1210
BibRef

Huber, M.F.[Marco F.],
Recursive Gaussian process: On-line regression and learning,
PRL(45), No. 1, 2014, pp. 85-91.
Elsevier DOI 1407
Gaussian processes BibRef

Zhu, W.[Wentao], Miao, J.[Jun], Qing, L.Y.[Lai-Yun],
Robust regression with extreme support vectors,
PRL(45), No. 1, 2014, pp. 205-210.
Elsevier DOI 1407
Extreme Support Vector Regression BibRef

Schaeben, H.[Helmut],
Targeting: Logistic Regression, Special Cases and Extensions,
IJGI(3), No. 4, 2014, pp. 1387-1411.
DOI Link 1412
Recover the true conditional probabilities if the joint distribution. BibRef

Zou, B.[Bin], Tang, Y.Y.[Yuan Yan], Xu, Z.B.[Zong-Ben], Li, L.Q.[Luo-Qing], Xu, J.[Jie], Lu, Y.[Yang],
The Generalization Performance of Regularized Regression Algorithms Based on Markov Sampling,
Cyber(44), No. 9, September 2014, pp. 1497-1507.
IEEE DOI 1410
Markov processes BibRef

Bootkrajang, J.[Jakramate], Kaban, A.[Ata],
Learning kernel logistic regression in the presence of class label noise,
PR(47), No. 11, 2014, pp. 3641-3655.
Elsevier DOI 1407
Classification BibRef

Yang, H., Jia, X., Patras, I., Chan, K.P.,
Random Subspace Supervised Descent Method for Regression Problems in Computer Vision,
SPLetters(22), No. 10, October 2015, pp. 1816-1820.
IEEE DOI 1506
Computer vision BibRef

Pan, Z.[Zheng], Zhang, C.S.[Chang-Shui],
Relaxed sparse eigenvalue conditions for sparse estimation via non-convex regularized regression,
PR(48), No. 1, 2015, pp. 231-243.
Elsevier DOI 1410
Sparse estimation BibRef

Munoz-Gonzalez, L., Lazaro-Gredilla, M., Figueiras-Vidal, A.R.,
Laplace Approximation for Divisive Gaussian Processes for Nonstationary Regression,
PAMI(38), No. 3, March 2016, pp. 618-624.
IEEE DOI 1602
Approximation algorithms BibRef

Pan, L.[Lili], Saragih, J.M.[Jason M.], Chu, W.S.[Wen-Sheng],
Mixture of grouped regressors and its application to visual mapping,
PR(53), No. 1, 2016, pp. 184-194.
Elsevier DOI 1602
Mixture of regressors BibRef

Nguyen, H.D., Lloyd-Jones, L.R., McLachlan, G.J.,
A Block Minorization-Maximization Algorithm for Heteroscedastic Regression,
SPLetters(23), No. 8, August 2016, pp. 1131-1135.
IEEE DOI 1608
Big Data BibRef

Li, X., Zheng, J.,
Active Learning for Regression With Correlation Matching and Labeling Error Suppression,
SPLetters(23), No. 8, August 2016, pp. 1081-1085.
IEEE DOI 1608
compressed sensing BibRef

Hong, Y.[Yi], Kwitt, R.[Roland], Singh, N.[Nikhil], Vasconcelos, N.M.[Nuno M.], Niethammer, M.[Marc],
Parametric Regression on the Grassmannian,
PAMI(38), No. 11, November 2016, pp. 2284-2297.
IEEE DOI 1610
Jacobian matrices BibRef

Hong, Y.[Yi], Kwitt, R.[Roland], Singh, N.[Nikhil], Davis, B.[Brad], Vasconcelos, N.M.[Nuno M.], Niethammer, M.[Marc],
Geodesic Regression on the Grassmannian,
ECCV14(II: 632-646).
Springer DOI 1408
BibRef

Gu, Y.X.[Ying-Xin], Wylie, B.K.[Bruce K.], Boyte, S.P.[Stephen P.], Picotte, J.[Joshua], Howard, D.M.[Daniel M.], Smith, K.[Kelcy], Nelson, K.J.[Kurtis J.],
An Optimal Sample Data Usage Strategy to Minimize Overfitting and Underfitting Effects in Regression Tree Models Based on Remotely-Sensed Data,
RS(8), No. 11, 2016, pp. 943.
DOI Link 1612
BibRef

Neyshabouri, M.M.[Mohammadreza Mohaghegh], Demir, O.[Oguzhan], Delibalta, I.[Ibrahim], Kozat, S.S.[Suleyman Serdar],
Highly efficient nonlinear regression for big data with lexicographical splitting,
SIViP(11), No. 3, March 2017, pp. 391-398.
Springer DOI 1702
BibRef

Zhang, Z., Lai, Z., Xu, Y., Shao, L., Wu, J., Xie, G.S.,
Discriminative Elastic-Net Regularized Linear Regression,
IP(26), No. 3, March 2017, pp. 1466-1481.
IEEE DOI 1703
image classification BibRef

Tehrani, A.F.[Ali Fallah], Ahrens, D.[Diane],
Modeling label dependence for multi-label classification using the Choquistic regression,
PRL(92), No. 1, 2017, pp. 75-80.
Elsevier DOI 1705
Choquet integral BibRef

Golay, J.[Jean], Leuenberger, M.[Michael], Kanevski, M.[Mikhail],
Feature selection for regression problems based on the Morisita estimator of intrinsic dimension,
PR(70), No. 1, 2017, pp. 126-138.
Elsevier DOI 1706
Feature, selection BibRef

Kim, K.[Kyoungok], Hong, J.S.[Jung-Sik],
A hybrid decision tree algorithm for mixed numeric and categorical data in regression analysis,
PRL(98), No. 1, 2017, pp. 39-45.
Elsevier DOI 1710
Mixed data BibRef

Gillis, N., Luce, R.,
A Fast Gradient Method for Nonnegative Sparse Regression With Self-Dictionary,
IP(27), No. 1, January 2018, pp. 24-37.
IEEE DOI 1712
convex programming, gradient methods, matrix decomposition, regression analysis, NMF, conic basis columns, data matrix, sparse regression BibRef

Zhen, X.T.[Xian-Tong], Yu, M.Y.[Meng-Yang], He, X.F.[Xiao-Fei], Li, S.[Shuo],
Multi-Target Regression via Robust Low-Rank Learning,
PAMI(40), No. 2, February 2018, pp. 497-504.
IEEE DOI 1801
Biomedical imaging, Computational modeling, Correlation, Data models, Kernel, Optimization, Robustness, multi-target regression BibRef

Vanli, N.D.[N. Denizcan], Sayin, M.O.[Muhammed O.], Mohaghegh, N.M.[N. Mohammadreza], Ozkan, H.[Huseyin], Kozat, S.S.[Suleyman S.],
Nonlinear regression via incremental decision trees,
PR(86), 2019, pp. 1-13.
Elsevier DOI 1811
Online regression, Sequential learning, Nonlinear models, Incremental decision trees BibRef

Ma, Z.[Zhongchen], Chen, S.C.[Song-Can],
A convex formulation for multiple ordinal output classification,
PR(86), 2019, pp. 73-84.
Elsevier DOI 1811
Multiple ordinal output classification, Multiple discrete ordinal variables, Ordinal regression, Convex function BibRef

Flores, S.A.,
Robustness of L_1-Norm Estimation: From Folklore to Fact,
SPLetters(25), No. 11, November 2018, pp. 1640-1644.
IEEE DOI 1811
estimation theory, regression analysis, signal processing, data analysis procedures, robust statistic viewpoint, robust regression BibRef

Karbalayghareh, A., Qian, X., Dougherty, E.R.,
Optimal Bayesian Transfer Regression,
SPLetters(25), No. 11, November 2018, pp. 1655-1659.
IEEE DOI 1811
Bayes methods, learning (artificial intelligence), mean square error methods, pattern classification, transfer learning BibRef

Tao, J.W.[Jian-Wen], Zhou, D.[Di], Liu, F.Y.[Fang-Yu], Zhu, B.[Bin],
Latent multi-feature co-regression for visual recognition by discriminatively leveraging multi-source models,
PR(87), 2019, pp. 296-316.
Elsevier DOI 1812
Multi-source adaptation, Multi-feature representation, Latent space, Group sparsity BibRef

Li, C.S.[Chang-Sheng], Wei, F.[Fan], Dong, W.S.[Wei-Shan], Wang, X.F.[Xiang-Feng], Liu, Q.S.[Qing-Shan], Zhang, X.[Xin],
Dynamic Structure Embedded Online Multiple-Output Regression for Streaming Data,
PAMI(41), No. 2, February 2019, pp. 323-336.
IEEE DOI 1901
Data models, Linear programming, Correlation, Current measurement, Prediction algorithms, Load modeling, lossless compression BibRef

Zhang, X.W.[Xiao-Wei], Shi, X.D.[Xu-Dong], Sun, Y.[Yu], Cheng, L.[Li],
Multivariate Regression with Gross Errors on Manifold-Valued Data,
PAMI(41), No. 2, February 2019, pp. 444-458.
IEEE DOI 1901
Manifolds, Kernel, Multivariate regression, Data models, Optimization, Diffusion tensor imaging, Shape, diffusion tensor imaging BibRef

Yang, M.[Muli], Deng, C.[Cheng], Nie, F.P.[Fei-Ping],
Adaptive-weighting discriminative regression for multi-view classification,
PR(88), 2019, pp. 236-245.
Elsevier DOI 1901
Multi-view learning, Supervised learning, Classification BibRef

Cui, J.R.[Jin-Rong], Zhu, Q.[Qi], Wang, D.[Ding], Li, Z.[Zuoyong],
Learning robust latent representation for discriminative regression,
PRL(117), 2019, pp. 193-200.
Elsevier DOI 1901
Discriminative representation, Linear regression, Sparse representation, Latent structure, Image recognition BibRef

Pillonetto, G.[Gianluigi], Schenato, L.[Luca], Varagnolo, D.[Damiano],
Distributed Multi-Agent Gaussian Regression via Finite-Dimensional Approximations,
PAMI(41), No. 9, Sep. 2019, pp. 2098-2111.
IEEE DOI 1908
Kernel, Estimation, Eigenvalues and eigenfunctions, Bayes methods, Noise measurement, Complexity theory, Gaussian processes, average consensus BibRef

Zbonáková, L.[Lenka], Monti, R.P.[Ricardo Pio], Härdle, W.K.[Wolfgang Karl],
Towards the interpretation of time-varying regularization parameters in streaming penalized regression models,
PRL(125), 2019, pp. 542-548.
Elsevier DOI 1909
Penalized regression, Non-stationarity, Regularization, Streaming data BibRef

Xiang, M., Xia, Y., Mandic, D.P.,
Complementary Cost Functions for Complex and Quaternion Widely Linear Estimation,
SPLetters(26), No. 9, September 2019, pp. 1344-1348.
IEEE DOI 1909
least squares approximations, mean square error methods, regression analysis, signal processing, least squares BibRef

Adsuara, J.E.[Jose E.], Pérez-Suay, A.[Adrián], Muńoz-Marí, J.[Jordi], Mateo-Sanchis, A.[Anna], Piles, M.[Maria], Camps-Valls, G.[Gustau],
Nonlinear Distribution Regression for Remote Sensing Applications,
GeoRS(57), No. 12, December 2019, pp. 10025-10035.
IEEE DOI Code:
HTML Version. 1912
Kernel, Remote sensing, Agriculture, Nonlinear optics, Optical imaging, Optical sensors, MODIS, vegetation optical depth (VOD) BibRef

Kayabol, K.[Koray],
Approximate Sparse Multinomial Logistic Regression for Classification,
PAMI(42), No. 2, February 2020, pp. 490-493.
IEEE DOI 2001
Logistics, Hyperspectral imaging, Approximation algorithms, Taylor series, Standards, Estimation, Convergence, classification BibRef

Liu, J.J.[Jian-Jun], Wu, Z.B.[Ze-Bin], Xiao, L.[Liang], Sun, J.[Jun], Yan, H.[Hong],
Generalized Tensor Regression for Hyperspectral Image Classification,
GeoRS(58), No. 2, February 2020, pp. 1244-1258.
IEEE DOI 2001
Hyperspectral imaging, Imaging, Training, Urban areas, Sun, Column generation (CG), hyperspectral image classification, tensor regression BibRef

Han, N., Wu, J., Fang, X., Wong, W.K., Xu, Y., Yang, J., Li, X.,
Double Relaxed Regression for Image Classification,
CirSysVideo(30), No. 2, February 2020, pp. 307-319.
IEEE DOI 2002
Training, Task analysis, Machine learning, Kernel, Computer science, Fasteners, Linear programming, Regression, image classification, computer vision BibRef

Wen, J., Zhong, Z., Zhang, Z., Fei, L., Lai, Z., Chen, R.,
Adaptive Locality Preserving Regression,
CirSysVideo(30), No. 1, January 2020, pp. 75-88.
IEEE DOI 2002
feature extraction, image classification, regression analysis, feature selection, target learning technique, supervised graph regularization BibRef

Dedecius, K., Žemlicka, R.,
Sequential Poisson Regression in Diffusion Networks,
SPLetters(27), 2020, pp. 625-629.
IEEE DOI 2005
Estimation, Bayes methods, Gaussian distribution, Random variables, Inference algorithms, Predictive models, Poisson regression BibRef

Adeli, E.[Ehsan], Li, X.R.[Xiao-Rui], Kwon, D.J.[Dong-Jin], Zhang, Y.[Yong], Pohl, K.M.[Kilian M.],
Logistic Regression Confined by Cardinality-Constrained Sample and Feature Selection,
PAMI(42), No. 7, July 2020, pp. 1713-1728.
IEEE DOI 2006
Training, Feature extraction, Logistics, Task analysis, Noise measurement, Computational modeling, Visualization, Sparsity, logistic regression BibRef

Singla, M.[Manisha], Ghosh, D.[Debdas], Shukla, K.K., Pedrycz, W.[Witold],
Robust twin support vector regression based on rescaled Hinge loss,
PR(105), 2020, pp. 107395.
Elsevier DOI 2006
Twin support vector regression, Correntropy, Gaussian noise, Outliers, Linear kernel, Non-linear kernels, Res-TSVR BibRef

Zou, Y.[Yun], Kuang, Y.[Yan], Zhi, Y.[Yue], Qu, X.B.[Xiao-Bo],
Investigation on linearisation of data-driven transport research: Two representative case studies,
IET-ITS(14), No. 7, July 2020, pp. 675-683.
DOI Link 2006
BibRef

Chung, S.[Seokhyun], Park, Y.W.[Young Woong], Cheong, T.[Taesu],
A mathematical programming approach for integrated multiple linear regression subset selection and validation,
PR(108), 2020, pp. 107565.
Elsevier DOI 2008
Regression diagnostics, Subset selection, Mathematical programming BibRef

Shen, X.J.[Xiang-Jun], Ni, C.G.[Cheng-Gong], Wang, L.J.[Liang-Jun], Zha, Z.J.[Zheng-Jun],
SLiKER: Sparse loss induced kernel ensemble regression,
PR(109), 2021, pp. 107587.
Elsevier DOI 2009
Multiple kernels, Ensemble regression, Sparse loss, Classification BibRef

Peng, L., Tsakiris, M.C.,
Linear Regression Without Correspondences via Concave Minimization,
SPLetters(27), 2020, pp. 1580-1584.
IEEE DOI 2009
Minimization, Upper bound, Signal processing algorithms, Linear regression, Linear programming, Sensors, Estimation, linear assignment problem BibRef

Iwata, D.[Daichi], Waechter, M.[Michael], Lin, W.Y.[Wen-Yan], Matsushita, Y.[Yasuyuki],
An Analysis of Sketched IRLS for Accelerated Sparse Residual Regression,
ECCV20(XII: 609-626).
Springer DOI 2010
Code, Least Squares.
WWW Link. BibRef

Zhang, L., Du, Y., Li, X., Zhen, X.,
Calibrated Multivariate Regression Networks,
CirSysVideo(30), No. 11, November 2020, pp. 4222-4231.
IEEE DOI 2011
Kernel, Multivariate regression, Correlation, Feature extraction, Task analysis, Calibration, Neural networks, low rank BibRef

Wang, X.L.[Xian-Liang], Du, J.[Jiao], Xu, G.X.[Guo-Xia], Passero, I.[Ignazio], Wang, H.[Hao], Yu, Y.F.[Yu-Feng],
Kernelized dual regression incorporating local information for image set classification,
PRL(140), 2020, pp. 274-280.
Elsevier DOI 2012
Affine hull, Distance metric, Image set classification, Kernelized dual regression BibRef

Liu, Z.[Ziang], Jiang, X.[Xue], Luo, H.[Hanbin], Fang, W.[Weili], Liu, J.[Jiajing], Wu, D.[Dongrui],
Pool-based unsupervised active learning for regression using iterative representativeness-diversity maximization (iRDM),
PRL(142), 2021, pp. 11-19.
Elsevier DOI 2101
Active learning, Regression, Unsupervised learning BibRef

Zhang, L.[Le], Shi, Z.L.[Zeng-Lin], Cheng, M.M.[Ming-Ming], Liu, Y.[Yun], Bian, J.W.[Jia-Wang], Zhou, J.T.[Joey Tianyi], Zheng, G.Y.[Guo-Yan], Zeng, Z.[Zeng],
Nonlinear Regression via Deep Negative Correlation Learning,
PAMI(43), No. 3, March 2021, pp. 982-998.
IEEE DOI 2102
BibRef
And: Correction: PAMI(43), No. 6, June 2021, pp. 2172-2172.
IEEE DOI 2106
Task analysis, Estimation, Training, Correlation, Computational modeling, Deep learning, convolutional neural network BibRef

Liu, J.[Jiani], Zhu, C.[Ce], Long, Z.[Zhen], Huang, H.[Huyan], Liu, Y.P.[Yi-Peng],
Low-rank tensor ring learning for multi-linear regression,
PR(113), 2021, pp. 107753.
Elsevier DOI 2103
Multilinear regression, Ridge regression, Tensor ring decomposition BibRef

Mastelini, S.M.[Saulo Martiello], de Carvalho, A.C.P.D.F.[Andre Carlos Ponce De_Leon Ferreira],
Using dynamical quantization to perform split attempts in online tree regressors,
PRL(145), 2021, pp. 37-42.
Elsevier DOI 2104
Online regression, Incremental regression, Decision trees, Hoeffding trees BibRef

Zhang, C.[Chao], Li, H.X.[Hua-Xiong], Qian, Y.H.[Yu-Hua], Chen, C.L.[Chun-Lin], Gao, Y.[Yang],
Pairwise Relations Oriented Discriminative Regression,
CirSysVideo(31), No. 7, July 2021, pp. 2646-2660.
IEEE DOI 2107
Transforms, Training data, Minimization, Linear regression, Integrated circuits, Pattern recognition, Training, classification BibRef

Degeest, A.[Alexandra], Frénay, B.[Benoît], Verleysen, M.[Michel],
Reading grid for feature selection relevance criteria in regression,
PRL(148), 2021, pp. 92-99.
Elsevier DOI 2107
Feature selection, Relevance criteria, Regression BibRef

Adiyeke, E.[Esra], Baydogan, M.G.[Mustafa Gökçe],
An ensemble-based semi-supervised feature ranking for multi-target regression problems,
PRL(148), 2021, pp. 36-42.
Elsevier DOI 2107
Semi-supervised learning, Feature ranking, Multi-target regression BibRef

Fallah Tehrani, A.[Ali],
Heuristics-Based Learning Approach for Choquistic Regression Models,
PRL(149), 2021, pp. 137-142.
Elsevier DOI 2108
Choquet integral, Choquistic regression, Efficiency, Complexity reduction, Predictive models BibRef

Woodbridge, Y.[Yonatan], Elidan, G.[Gal], Wiesel, A.[Ami],
Convex Nonparanormal Regression,
SPLetters(28), 2021, pp. 1680-1684.
IEEE DOI 2109
Dictionaries, Optimization, Maximum likelihood estimation, Convex functions, Training, Linear regression, convex optimization BibRef

Messoudi, S.[Soundouss], Destercke, S.[Sébastien], Rousseau, S.[Sylvain],
Copula-based conformal prediction for multi-target regression,
PR(120), 2021, pp. 108101.
Elsevier DOI 2109
Inductive conformal prediction, Copula functions, Multi-target regression, Deep neural networks, Random forests BibRef

Wang, Z.[Zheng], Nie, F.P.[Fei-Ping], Zhang, C.[Canyu], Wang, R.[Rong], Li, X.L.[Xue-Long],
Joint nonlinear feature selection and continuous values regression network,
PRL(150), 2021, pp. 197-206.
Elsevier DOI 2109
Nonlinear feature selection, Continuous values regression, -Norm regularized hidden layer, Re-weighted back propagation optimization algorithm BibRef


Meyer, G.P.[Gregory P.],
An Alternative Probabilistic Interpretation of the Huber Loss,
CVPR21(5257-5265)
IEEE DOI 2111
Toy manufacturing industry, Detectors, Probabilistic logic, Search problems, Pattern recognition, Object recognition BibRef

Li, W.H.[Wan-Hua], Huang, X.K.[Xiao-Ke], Lu, J.W.[Ji-Wen], Feng, J.J.[Jian-Jiang], Zhou, J.[Jie],
Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression,
CVPR21(13891-13900)
IEEE DOI 2111
Visualization, Uncertainty, Estimation, Gaussian distribution, Predictive models, Probabilistic logic, Data models BibRef

Sick, B.[Beate], Hathorn, T.[Torsten], Dürr, O.[Oliver],
Deep transformation models: Tackling complex regression problems with neural network based transformation models,
ICPR21(2476-2481)
IEEE DOI 2105
Deep learning, Maximum likelihood estimation, Uncertainty, Neural networks, Medical services, Predictive models, Probabilistic logic BibRef

Huang, M.,
Theory and Implementation of linear regression,
CVIDL20(210-217)
IEEE DOI 2102
regression analysis, sales management, linear regression, mathematical technique, food truck, Fitting, Deep learning, Food truck BibRef

Hou, J., Xue, L., Zhou, Y.,
A Mapping Data Prediction Algorithm based On Generalized Regression Neural Network,
CVIDL20(301-304)
IEEE DOI 2102
data analysis, neural nets, regression analysis, mapping data prediction algorithm, Border Router BibRef

Gustafsson, F.K.[Fredrik K.], Danelljan, M.[Martin], Schön, T.B.[Thomas B.],
Accurate 3D Object Detection using Energy-Based Models,
WAD21(2849-2858)
IEEE DOI 2109
Measurement, Solid modeling, Laser radar, Navigation, Object detection, Detectors BibRef

Gustafsson, F.K.[Fredrik K.], Danelljan, M.[Martin], Bhat, G.[Goutam], Schön, T.B.[Thomas B.],
Energy-based Models for Deep Probabilistic Regression,
ECCV20(XX:325-343).
Springer DOI 2011
BibRef

Pan, L.L.[Li-Li], Ai, S.J.[Shi-Jie], Ren, Y.Z.[Ya-Zhou], Xu, Z.L.[Zeng-Lin],
Self-paced Deep Regression Forests with Consideration on Underrepresented Examples,
ECCV20(XXX: 271-287).
Springer DOI 2010
BibRef

Liu, Y., Wang, F., Kong, W.A.,
Probabilistic Deep Ordinal Regression Based on Gaussian Processes,
ICCV19(5300-5308)
IEEE DOI 2004
convolutional neural nets, Gaussian processes, gradient methods, learning (artificial intelligence), pattern classification, Logistics BibRef

Hwang, J.J.[Jyh-Jing], Ke, T.W.[Tsung-Wei], Shi, J.B.[Jian-Bo], Yu, S.X.[Stella X.],
Adversarial Structure Matching for Structured Prediction Tasks,
CVPR19(4051-4060).
IEEE DOI 2002
BibRef

Diaz, R.[Raul], Marathe, A.[Amit],
Soft Labels for Ordinal Regression,
CVPR19(4733-4742).
IEEE DOI 2002
BibRef

Ahn, K.[Kyungmin], Tucker, J.D.[J. Derek], Wu, W.[Wei], Srivastava, A.[Anuj],
Elastic Handling of Predictor Phase in Functional Regression Models,
Diff-CVML18(437-4377)
IEEE DOI 1812
Predictive models, Measurement, Data models, Standards, Shape, Estimation, Pattern recognition BibRef

Mallasto, A., Feragen, A.,
Wrapped Gaussian Process Regression on Riemannian Manifolds,
CVPR18(5580-5588)
IEEE DOI 1812
Manifolds, Gaussian processes, Uncertainty, Kernel, Gaussian distribution, Shape, Measurement BibRef

Wen, J., Fei, L., Lai, Z., Zhang, Z., Wu, J., Fang, X.,
Adaptive Locality Preserving based Discriminative Regression,
ICPR18(535-540)
IEEE DOI 1812
Linear regression, Adaptation models, Training data, Robustness, Laplace equations, Linear regression, adaptive locality preserving BibRef

Schreiter, C., Sun, J., Schelkens, P.,
Image Reconstruction with Smoothed Mixtures of Regressions,
ICIP18(400-404)
IEEE DOI 1809
Kernel, Image reconstruction, Mixture models, Estimation, Data models, Density functional theory, Adaptation models, expectation-maximization BibRef

Martins, P., Batista, I.J.,
Simultaneous Cascaded Regression,
ICIP18(181-185)
IEEE DOI 1809
Shape, Jacobian matrices, Optimization, Face, Computational modeling, Integrated circuits, Deformable models, cascaded regression BibRef

Zhang, Z., Zhong, Z., Cui, J., Fei, L.,
Learning robust latent subspace for discriminative regression,
VCIP17(1-4)
IEEE DOI 1804
feature extraction, learning (artificial intelligence), mathematics computing, optimisation, pattern classification, sparse BibRef

Wu, H., Spurlock, S., Souvenir, R.,
Semi-supervised multi-output image manifold regression,
ICIP17(2413-2417)
IEEE DOI 1803
Kernel, Manifolds, Mathematical model, Prediction algorithms, Principal component analysis, Task analysis, semi-supervised BibRef

Berthoumieu, Y., Bombrun, L., Germain, C., Said, S.,
Classification approach based on the product of riemannian manifolds from Gaussian parametrization space,
ICIP17(206-210)
IEEE DOI 1803
Covariance matrices, Databases, Gaussian distribution, Manifolds, Measurement, Task analysis, Visualization, Classification, image local descriptors BibRef

Muralidharan, P., Hinkle, J., Fletcher, P.T.,
A map estimation algorithm for Bayesian polynomial regression on riemannian manifolds,
ICIP17(215-219)
IEEE DOI 1803
Bayes methods, Data models, Manifolds, Maximum likelihood estimation, Rats, Shape BibRef

Gorbach, N.S.[Nico S.], Bian, A.A.[Andrew An], Fischer, B.[Benjamin], Bauer, S.[Stefan], Buhmann, J.M.[Joachim M.],
Model Selection for Gaussian Process Regression,
GCPR17(306-318).
Springer DOI 1711
BibRef

Huang, D., Han, L., de la Torre, F.[Fernando],
Soft-Margin Mixture of Regressions,
CVPR17(4058-4066)
IEEE DOI 1711
Estimation, Gaussian processes, Ground penetrating radar, Kernel, Magnetic resonance, Training BibRef

Maier, A., Rodríguez-Salas, D.,
Fast and robust selection of highly-correlated features in regression problems,
MVA17(482-485)
DOI Link 1708
Algorithm design and analysis, Correlation, Linear regression, Prediction algorithms, Robustness, Search problems, Weight, measurement BibRef

Luo, L.[Lei], Tu, Q.H.[Qing-Hua], Yang, J.[Jian], Zhang, Y.G.[Yi-Gong],
Dual approximated nuclear norm based matrix regression via adaptive line search scheme,
ICPR16(2538-2543)
IEEE DOI 1705
Acceleration, Convergence, Face recognition, Matrix converters, Nuclear magnetic resonance, Robustness, adaptive line search scheme, continuously differentiable, face recognition, gradient method, nuclear, norm, based, matrix, regression BibRef

Fagundes, R.A.A., de Souza, R.M.C.R.[Renata M.C.R.], Soares, Y.M.G.,
Quantile regression of interval-valued data,
ICPR16(2586-2591)
IEEE DOI 1705
Analytical models, Computational modeling, Data models, Linear regression, Mathematical model, Predictive models, Production BibRef

Subramanian, S., Rana, S., Gupta, S., Sivakumar, P.B., Velayutham, C.S., Venkateshc, S.,
Bayesian nonparametric Multiple Instance Regression,
ICPR16(3661-3666)
IEEE DOI 1705
Agriculture, Bayes methods, Clustering algorithms, Computational modeling, Data models, Prediction algorithms, Predictive, models BibRef

Xing, C., Geng, X., Xue, H.,
Logistic Boosting Regression for Label Distribution Learning,
CVPR16(4489-4497)
IEEE DOI 1612
BibRef

Feng, Q., Zhou, Y., Lan, R.,
Pairwise Linear Regression Classification for Image Set Retrieval,
CVPR16(4865-4872)
IEEE DOI 1612
BibRef

Banerjee, M.[Monami], Chakraborty, R.[Rudrasis], Ofori, E.[Edward], Okun, M.S.[Michael S.], Vaillancourt, D.E.[David E.], Vemuri, B.C.[Baba C.],
A Nonlinear Regression Technique for Manifold Valued Data with Applications to Medical Image Analysis,
CVPR16(4424-4432)
IEEE DOI 1612
BibRef

Wang, Y.X.[Yu-Xiong], Hebert, M.[Martial],
Learning to Learn: Model Regression Networks for Easy Small Sample Learning,
ECCV16(VI: 616-634).
Springer DOI 1611
BibRef

Robles-Kelly, A.[Antonio],
Least-Squares Regression with Unitary Constraints for Network Behaviour Classification,
SSSPR16(26-36).
Springer DOI 1611
BibRef

Koda, S.[Satoru],
Adaptive Sparse Bayesian Regression with Variational Inference for Parameter Estimation,
SSSPR16(263-273).
Springer DOI 1611
BibRef

Antonakos, E., Snape, P., Trigeorgis, G., Zafeiriou, S.P.[Stefanos P.],
Adaptive cascaded regression,
ICIP16(1649-1653)
IEEE DOI 1610
Active appearance model BibRef

Gurovich, Y., Kissos, I., Hanani, Y.,
Quality scores for deep regression systems,
ICIP16(3758-3762)
IEEE DOI 1610
Face BibRef

Perez-Pellitero, E., Salvador, J., Ruiz-Hidalgo, J., Rosenhahn, B.,
Half hypersphere confinement for piecewise linear regression,
WACV16(1-9)
IEEE DOI 1606
Euclidean distance BibRef

Belagiannis, V., Rupprecht, C., Carneiro, G., Navab, N.,
Robust Optimization for Deep Regression,
ICCV15(2830-2838)
IEEE DOI 1602
Convergence; Machine learning; Minimization; Robustness; Training BibRef

Pandremmenou, K.[Katerina], Shahid, M.[Muhammad], Kondi, L.P.[Lisimachos P.], Lovstrom, B.[Benny],
On the improvement of no-reference mean opinion score estimation accuracy by following a frame-level regression approach,
ICIP15(1850-1854)
IEEE DOI 1512
Estimation accuracy BibRef

Wang, Y.[Yin], Dicle, C.[Caglayan], Sznaier, M.[Mario], Camps, O.I.[Octavia I.],
Self Scaled Regularized Robust Regression,
CVPR15(3261-3269)
IEEE DOI 1510
BibRef

Nilsson, M.[Mikael],
Elastic Net Regularized Logistic Regression Using Cubic Majorization,
ICPR14(3446-3451)
IEEE DOI 1412
Convergence BibRef

Saragih, J.M.[Jason M.],
Principal regression analysis,
CVPR11(2881-2888).
IEEE DOI 1106
Multivariate regression. For non-rigid face and car alignment. BibRef

Raman, S.[Sudhir], Roth, V.[Volker],
Sparse Point Estimation for Bayesian Regression via Simulated Annealing,
DAGM12(317-326).
Springer DOI 1209
BibRef
Earlier:
Sparse Bayesian Regression for Grouped Variables in Generalized Linear Models,
DAGM09(242-251).
Springer DOI 0909
BibRef

Hong, Y.[Yi], Shi, Y.[Yundi], Styner, M.[Martin], Sanchez, M.[Mar], Niethammer, M.[Marc],
Simple Geodesic Regression for Image Time-Series,
WBIR12(11-20).
Springer DOI 1208
BibRef

Kramer, O.[Oliver],
On Evolutionary Approaches to Unsupervised Nearest Neighbor Regression,
EvoIASP12(346-355).
Springer DOI 1204
BibRef

Mitra, K.[Kaushik], Veeraraghavan, A.[Ashok], Chellappa, R.[Rama],
Robust RVM regression using sparse outlier model,
CVPR10(1887-1894).
IEEE DOI 1006
Relevance Vector Machine. BibRef

Yang, G.[Gelan], Xu, X.[Xue], Jin, H.X.[Hui-Xia],
Semi-Supervised Regression via Local Block Coordinate,
CISP09(1-4).
IEEE DOI 0910
BibRef

Ding, L.[Lei], Bai, X.L.[Xiao-Le],
Adaptive Laplacian eigenfunctions as bases for regression analysis,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
MRF Optimization, Energy Minimization .


Last update:Nov 30, 2021 at 22:19:38