Semi-supervised regression: A recent review

[1]  Xiaoyan Sun,et al.  Interactive genetic algorithms with large population and semi-supervised learning , 2012, Appl. Soft Comput..

[2]  Miguel Á. Carreira-Perpiñán,et al.  Semi-supervised regression with temporal image sequences , 2010, 2010 IEEE International Conference on Image Processing.

[3]  Nikos A. Vlassis,et al.  Gaussian fields for semi-supervised regression and correspondence learning , 2006, Pattern Recognit..

[4]  N. Altman.  An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[5]  Choujun Zhan,et al.  Image classification via least square semi-supervised discriminant analysis with flexible kernel regression for out-of-sample extension , 2015, Neurocomputing.

[6]  E. Mammen,et al.  Comparing Nonparametric Versus Parametric Regression Fits , 1993 .

[7]  Wei Chu,et al.  Gaussian Processes for Ordinal Regression , 2005, J. Mach. Learn. Res..

[8]  Zhi-Hua Zhou,et al.  Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[9]  Dit-Yan Yeung,et al.  Semi-Supervised Multi-Task Regression , 2009, ECML/PKDD.

[10]  Francisco Herrera,et al.  Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study , 2015, Knowledge and Information Systems.

[11]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[12]  Kuniaki Uehara,et al.  Graph-based Semi-Supervised Regression and Its Extensions , 2015 .

[13]  Yukio Kosugi,et al.  Semi-Supervised Hyperspectral Subspace Learning Based on a Generalized Eigenvalue Problem for Regression and Dimensionality Reduction , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[14]  N. Aronszajn.  Theory of Reproducing Kernels. , 1950 .

[15]  Michel Verleysen,et al.  A graph Laplacian based approach to semi-supervised feature selection for regression problems , 2013, Neurocomputing.

[16]  Jason Weston,et al.  Transductive Inference for Estimating Values of Functions , 1999, NIPS.

[17]  Bernhard Schölkopf,et al.  Transductive Classification via Local Learning Regularization , 2007, AISTATS.

[18]  S. Newsam,et al.  IM2MAP: deriving maps from georeferenced community contributed photo collections , 2011, WSM '11.

[19]  Michael K. Ng,et al.  A semi-supervised regression model for mixed numerical and categorical variables , 2007, Pattern Recognit..

[20]  P. K. Srijith,et al.  Semi-supervised Gaussian Process Ordinal Regression , 2013, ECML/PKDD.

[21]  Shiliang Sun,et al.  A survey of multi-view machine learning , 2013, Neural Computing and Applications.

[22]  Friedhelm Schwenker,et al.  Combining committee-based semi-supervised learning and active learning , 2010 .

[23]  Alvaro Soto,et al.  Local feature selection using Gaussian process regression , 2014, Intell. Data Anal..

[24]  Florian Steinke,et al.  Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction , 2009, NIPS.

[25]  Thorsten Joachims,et al.  Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.

[26]  Florence d'Alché-Buc,et al.  Input Output Kernel Regression: Supervised and Semi-Supervised Structured Output Prediction with Operator-Valued Kernels , 2016, J. Mach. Learn. Res..

[27]  Francisco Herrera,et al.  On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification , 2014, Neurocomputing.

[28]  H. Abdi.  Partial least squares regression and projection on latent structure regression (PLS Regression) , 2010 .

[29]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[30]  Feiping Nie,et al.  Semi-Supervised Classification via Local Spline Regression , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Xili Wang,et al.  Semi-supervised support vector regression model for remote sensing water quality retrieving , 2011 .

[32]  Zhi-Hua Zhou,et al.  Tri-training: exploiting unlabeled data using three classifiers , 2005, IEEE Transactions on Knowledge and Data Engineering.

[33]  Sungzoon Cho,et al.  Semi-supervised support vector regression based on self-training with label uncertainty: An application to virtual metrology in semiconductor manufacturing , 2016, Expert Syst. Appl..

[34]  David Yarowsky,et al.  Unsupervised Word Sense Disambiguation Rivaling Supervised Methods , 1995, ACL.

[35]  Ivor W. Tsang,et al.  Transductive Ordinal Regression , 2011, IEEE Transactions on Neural Networks and Learning Systems.

[36]  Hancan Zhu,et al.  The convergence rate of semi-supervised regression with quadratic loss , 2018, Appl. Math. Comput..

[37]  Xiaofei He,et al.  Semi-supervised Regression via Parallel Field Regularization , 2011, NIPS.

[38]  Hsuan-Tien Lin.  A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods , 2005 .

[39]  E. Nadaraya.  On Estimating Regression , 1964 .

[40]  Michelangelo Ceci,et al.  Self-training for multi-target regression with tree ensembles , 2017, Knowl. Based Syst..

[41]  Tommy W. S. Chow,et al.  Learning from normalized local and global discriminative information for semi-supervised regression and dimensionality reduction , 2015, Inf. Sci..

[42]  Zhiqiang Ge,et al.  Co-training partial least squares model for semi-supervised soft sensor development , 2015 .

[43]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[44]  Tae-Kyun Kim,et al.  Real-Time Articulated Hand Pose Estimation Using Semi-supervised Transductive Regression Forests , 2013, 2013 IEEE International Conference on Computer Vision.

[45]  Taghi M. Khoshgoftaar,et al.  Deep learning applications and challenges in big data analytics , 2015, Journal of Big Data.

[46]  Michael Andrew Christie,et al.  GEOMODELLING OF A FLUVIAL SYSTEM WITH SEMI-SUPERVISED SUPPORT VECTOR REGRESSION , 2008 .

[47]  Daniele Marinazzo,et al.  Semi-supervised learning by search of optimal target vector , 2008, Pattern Recognit. Lett..