Verbeek, J.J.[Jakob J.],
Vlassis, N.[Nikos],
Gaussian fields for semi-supervised regression and correspondence
learning,
PR(39), No. 10, October 2006, pp. 1864-1875.
Elsevier DOI Gaussian fields; Regression; Active learning; Model selection
0606
BibRef
Ng, M.K.[Michael K.],
Chan, E.Y.[Elaine Y.],
So, M.M.C.[Meko M.C.],
Ching, W.K.[Wai-Ki],
A semi-supervised regression model for mixed numerical and categorical
variables,
PR(40), No. 6, June 2007, pp. 1745-1752.
Elsevier DOI
0704
Clustering; Regression; Data mining; Numerical variables; Categorical variables
BibRef
Zhao, M.B.[Ming-Bo],
Zhan, C.J.[Chou-Jun],
Wu, Z.[Zhou],
Tang, P.[Peng],
Semi-Supervised Image Classification Based on Local and Global
Regression,
SPLetters(22), No. 10, October 2015, pp. 1666-1670.
IEEE DOI
1506
extrapolation
BibRef
Fazakis, N.[Nikos],
Karlos, S.[Stamatis],
Kotsiantis, S.[Sotiris],
Sgarbas, K.[Kyriakos],
A multi-scheme semi-supervised regression approach,
PRL(125), 2019, pp. 758-765.
Elsevier DOI
1909
Semi-supervised regression, Multi-scheme regression,
Semi-supervised learning, Ensemble method, Machine learning
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
Bao, J.Q.[Jia-Qi],
Kudo, M.[Mineichi],
Kimura, K.[Keigo],
Sun, L.[Lu],
Robust embedding regression for semi-supervised learning,
PR(145), 2024, pp. 109894.
Elsevier DOI
2311
Feature selection, Semi-supervised learning, Ridge regression, Nuclear norm
BibRef
Pan, Z.Y.[Zhi-Yu],
Cui, J.H.[Jia-Hao],
Wang, K.W.[Ke-Wei],
Wu, Y.Z.[Yi-Zheng],
Cao, Z.G.[Zhi-Guo],
Pseudo Label Fusion With Uncertainty Estimation for Semi-Supervised
Cropping Box Regression,
MultMed(26), 2024, pp. 8157-8171.
IEEE DOI
2408
Task analysis, Uncertainty, Annotations, Semisupervised learning,
Object detection, Data models, Multitasking, Image cropping,
uncertainty estimation
BibRef
Nitzan, Y.[Yotam],
Gal, R.[Rinon],
Brenner, O.[Ofir],
Cohen-Or, D.[Daniel],
LARGE: Latent-Based Regression through GAN Semantics,
CVPR22(19217-19227)
IEEE DOI
2210
Image coding, Semantics, Encoding, Reliability,
Task analysis, Representation learning,
Self- semi- meta- Transfer/low-shot/long-tail learning
BibRef
Wang, Y.K.[Yi-Kai],
Sun, X.W.[Xin-Wei],
Fu, Y.W.[Yan-Wei],
Scalable Penalized Regression for Noise Detection in Learning with
Noisy Labels,
CVPR22(346-355)
IEEE DOI
2210
Training, Statistical analysis, Pipelines, Training data,
Probabilistic logic, Data models, Robustness, Statistical methods,
Self- semi- meta- unsupervised learning
BibRef
Shukla, M.[Megh],
Bayesian Uncertainty and Expected Gradient Length - Regression: Two
Sides Of The Same Coin?,
WACV22(2021-2030)
IEEE DOI
2202
Uncertainty, Closed-form solutions,
Pose estimation, Approximation algorithms, Inference algorithms,
Semi- and Un- supervised Learning Active Learning
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
Xie, W.X.[Wen-Xuan],
Lu, Z.W.[Zhi-Wu],
Peng, Y.X.[Yu-Xin],
Xiao, J.G.[Jian-Guo],
Multimodal semi-supervised image classification by combining tag
refinement, graph-based learning and support vector regression,
ICIP13(4307-4311)
IEEE DOI
1402
Graph-based semi-supervised learning
BibRef
Carreira-Perpinan, M.A.[Miguel A.],
Lu, Z.D.[Zheng-Dong],
Parametric dimensionality reduction by unsupervised regression,
CVPR10(1895-1902).
IEEE DOI
1006
BibRef
Earlier:
Dimensionality reduction by unsupervised regression,
CVPR08(1-8).
IEEE DOI
0806
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
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
MRF Optimization, Energy Minimization .