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.[Nuno],
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.[Nuno],
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.[Fangyu],
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
Fosson, S.M.,
A Biconvex Analysis for Lasso L_1 Reweighting,
SPLetters(25), No. 12, December 2018, pp. 1795-1799.
IEEE DOI
1812
compressed sensing, convergence of numerical methods,
convex programming, iterative methods, regression analysis,
reweighting algorithms
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
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
Yoo, Y.,
Yun, S.,
Chang, H.J.,
Demiris, Y.,
Choi, J.Y.,
Variational Autoencoded Regression:
High Dimensional Regression of Visual Data on Complex Manifold,
CVPR17(2943-2952)
IEEE DOI
1711
Data models, Decoding, Gaussian processes, Image reconstruction,
Image sequences, Kernel, Visualization
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,
EvoIASP(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 .