Multiple-view multiple-learner active learning,
PR(43), No. 9, September 2010, pp. 3113-3119.
Elsevier DOI 1006
Multiple-view learning; Multiple-learner learning; Active learning; Artificial neural network BibRef
Xing, E.P.[Eric Poe],
Large-Margin Predictive Latent Subspace Learning for Multiview Data Analysis,
PAMI(34), No. 12, December 2012, pp. 2365-2378.
IEEE DOI 1210
A novel multi-view learning developed from single-view patterns,
PR(44), No. 10-11, October-November 2011, pp. 2395-2413.
Elsevier DOI 1101
Multi-view learning; Classifier design; Rademacher complexity; Ensemble learning; Ho-Kashyap classifier; Regularization learning; Pattern recognition See also Algorithm for Linear Inequalities and its Applications, An. BibRef
Three-fold structured classifier design based on matrix pattern,
PR(46), No. 6, June 2013, pp. 1532-1555.
Elsevier DOI 1302
Vector pattern; Matrix pattern; Global structure; Local structure; Classifier design; Pattern recognition BibRef
A unified dimensionality reduction framework for semi-paired and semi-supervised multi-view data,
PR(45), No. 5, May 2012, pp. 2005-2018.
Elsevier DOI 1201
Multi-view data; Correlation analysis; Semi-supervised learning; Semi-paired learning; Dimensionality reduction BibRef
Multi-View Learning With Incomplete Views,
IP(24), No. 12, December 2015, pp. 5812-5825.
IEEE DOI 1512
learning (artificial intelligence) BibRef
Block-Row Sparse Multiview Multilabel Learning for Image Classification,
Cyber(46), No. 2, February 2016, pp. 450-461.
IEEE DOI 1601
Multiview, multilabel BibRef
Uniform Projection for Multi-View Learning,
PAMI(39), No. 8, August 2017, pp. 1675-1689.
IEEE DOI 1707
Convergence, Distortion measurement, Eigenvalues and eigenfunctions, Kernel, Nonlinear distortion, Optimization, Multi-view learning, clustering, low-dimensional projection, unsupervised learning BibRef
Scalable Multi-View Semi-Supervised Classification via Adaptive Regression,
IP(26), No. 9, September 2017, pp. 4283-4296.
IEEE DOI 1708
image classification, learning (artificial intelligence), matrix algebra, minimisation, regression analysis, vectors, MVAR, adaptive optimized weight coefficient, adaptive regression, image processing, l2,1 matrix norm, machine learning application, nonsmooth l2,1-norm minimization problem, regression-based loss function, scalable multiview semisupervised classification problem, vector, Algorithm design and analysis, Computational modeling, Kernel, Minimization, Prediction algorithms, Semisupervised learning, Training, l2,1-norm minimization, Multi-view, classification, semi-supervised, learning BibRef
Convex Multiview Semi-Supervised Classification,
IP(26), No. 12, December 2017, pp. 5718-5729.
IEEE DOI 1710
hyperparameter elimination, local-minimum problem, multiview data context, optimization method, Data mining, Optimization methods, Semisupervised learning, BibRef
Learning Multiple Views with Orthogonal Denoising Autoencoders,
Springer DOI 1601
Adaptive Unsupervised Multi-view Feature Selection for Visual Concept Recognition,
Springer DOI 1304
Projectable classifiers for multi-view object class recognition,
IEEE DOI 1201
Gated classifiers: Boosting under high intra-class variation,
IEEE DOI 1106
A Novel Multi-view Agglomerative Clustering Algorithm Based on Ensemble of Partitions on Different Views,
IEEE DOI 1008
Fast Recognition of Multi-View Faces with Feature Selection,
IEEE DOI 0510
SVM based face recognition. BibRef
Improved estimation of hidden Markov model parameters from multiple observation sequences,
IEEE DOI 0211
Geometrical Learning from Multiple Stereo Views Through Monocular Based Feature Grouping,
IEEE DOI BibRef 9000
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Multi-Label Classification, Multilabel Classification .