Wah, B.W.[Benjamin W.],
Machine Learning of Computer Vision Algorithms,
HPRIP-CV94(319-359). BibRef 9400
Fu, K.S., ed.,
Pattern Recognition and Machine Learning,
PlenumPress, New York, 1971. BibRef 7100
Introduction to the Special Section on Learning in Computer Vision,
PAMI(16), No. 9, September 1994, pp. 865-867.
IEEE Top Reference. BibRef 9409
Introduction to the Special Issue on Learning in Computer Vision and Pattern Recognition,
SMC-B(35), No. 3, June 2005, pp. 391-396.
IEEE DOI 0508
The Nature of Statistical Learning Theory,
Springer-Verlag1996. BibRef 9600
Statistical Learning Theory,
John Wiley& Sons, 1998. BibRef 9800
An overview of statistical learning theory,
TNN(10), No. 5, 1999, pp. 988-999. 0906
Machine Learning Research: Four Current Directions,
AI Magazine(18), No. 4, 1997, pp. 97-136. BibRef 9700
Shelton, C.R.[Christian R.],
Machine Learning, Machine Vision, and the Brain,
AIMag(20), No. 3, Fall 1999, pp. 37-55. Regularization. Support Vector Machines. Survey, Learning. Survey of learning focused on a vision domain. Regularization, Support Vector Machines. Applied to face and pedestrian recognition. BibRef 9900
Learning in Pattern Recognition: Some Thoughts,
PRL(22), No. 1, January 2001, pp. 3-13.
Elsevier DOI 0105
Learning in Computer Vision: Some Thoughts,
Springer DOI 0711
3D Scene interpretation by combining probability theory and logic: The tower of knowledge,
CVIU(115), No. 11, November 2011, pp. 1581-1596.
Elsevier DOI 1110
Learning Logic Rules for Scene Interpretation Based on Markov Logic Networks,
Springer DOI 0909
Recursive Tower of Knowledge for Learning to Interpret Scenes,
PDF File. 0809
Scene labelling systems; Logic and probabilities; Machine learning; System architecture BibRef
Tower of Knowledge for scene interpretation: A survey,
PRL(48), No. 1, 2014, pp. 42-48.
Elsevier DOI 1410
Tower of Knowledge. Cue of human language, for scene interpretation BibRef
Freeman, W.T.[William T.],
Guest Editorial Machine Learning for Vision,
IJCV(77), No. 1-3, May 2008, pp. 1.
Springer DOI 0803
Learning to learn: From smart machines to intelligent machines,
PRL(29), No. 8, 1 June 2008, pp. 1024-1032.
Elsevier DOI 0804
Online Learning for Human-Robot Interaction,
IEEE DOI 0706
Incremental subspace learning based on Nonparametric Discriminant Analysis. Number of classes and samples not known and changes over time. Intelligent systems; Cognitive development; Context; Social robotics; Face recognition BibRef
Online pattern recognition and machine learning techniques for computer-vision: Theory and applications,
IVC(28), No. 7, July 2010, pp. 1063-1064.
Elsevier DOI 1006
Introduction to special issue. BibRef
Deep Learning Advances in Computer Vision with 3D Data: A Survey,
Surveys(50), No. 2, June 2017, pp. Article No 20.
DOI Link 1708
Survey, Deep Learning. This article surveys methods applying deep learning on 3D data and provides a classification based on how they exploit them. From the results of the examined works, we conclude that systems employing 2D views of 3D data typically surpass voxel-based (3D) deep models, which however, can perform better with more layers and severe data augmentation. Therefore, larger-scale datasets and increased resolutions are required. BibRef
Convolutional Neural Networks for Inverse Problems in Imaging: A Review,
SPMag(34), No. 6, November 2017, pp. 85-95.
IEEE DOI 1712
Survey, Convolutional Neural Networks. Computed tomography, Image reconstruction, Image resolution, Image segmentation, Inverse problems, Linear programming, Noise reduction BibRef
Deep Convolutional Neural Network for Inverse Problems in Imaging,
IP(26), No. 9, September 2017, pp. 4509-4522.
IEEE DOI 1708
computerised tomography, feedforward neural nets, image resolution, iterative methods, learning (artificial intelligence), medical image processing, BibRef
Deep Reinforcement Learning: A Brief Survey,
SPMag(34), No. 6, November 2017, pp. 26-38.
IEEE DOI 1712
Survey, Deep Learning. Artificial intelligence, Learning (artificial intelligence), Machine learning, Neural networks, Signal processing algorithms, Visualization BibRef
The Robustness of Deep Networks: A Geometrical Perspective,
SPMag(34), No. 6, November 2017, pp. 50-62.
IEEE DOI 1712
Classification, Machine learning, Neural networks, Robustness, Visualization BibRef
Guest Editors' Introduction to the Special Section on Learning with Shared Information for Computer Vision and Multimedia Analysis,
PAMI(40), No. 5, May 2018, pp. 1029-1031.
IEEE DOI 1804
Collaboration, Computer vision, Information sharing, Learning systems, Machine learning, Multimedia communication, Training data BibRef
Document analysis systems that improve with use,
IJDAR(23), No. 1, January 2020, pp. 13-29.
WWW Link. 2003
Estimation, Learning, and Adaptation: Systems That Improve with Use,
Springer DOI 1211
Persistent Issues in Learning and Estimation,
ICPR98(Vol I: 561-564).
IEEE DOI 9808
Binary neural networks: A survey,
PR(105), 2020, pp. 107281.
Elsevier DOI 2006
Binary neural network, Deep learning, Model compression, Network quantization, Model acceleration BibRef
Adversarial Examples on Object Recognition: A Comprehensive Survey,
Surveys(53), No. 3, June 2020, pp. xx-yy.
DOI Link 2007
Survey, Adversairal Networks. security, robustness, machine learning, Adversarial examples BibRef
Generative Adversarial Networks,
CACM(63), No. 11, November 2020, pp. 139-144.
DOI Link 2010
Survey, GAN. BibRef
Ward, T.E.[Tomas E.],
Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy,
Surveys(54), No. 2, February 2021, pp. xx-yy.
DOI Link 2104
Survey, GAN. Generative adversarial networks, loss-variants, computer vision, stabilizing training, architecture-variants BibRef
A Survey on Fuzzy Deep Neural Networks,
Surveys(53), No. 3, May 2020, pp. xx-yy.
DOI Link 2007
Survey, Deep Networks. parallel models, integrated models, sequential models, ensemble models, fuzzy systems, Deep architecture BibRef
A Survey on Bayesian Deep Learning,
Surveys(53), No. 5, September 2020, pp. xx-yy.
DOI Link 2010
generative models, Deep learning, Bayesian networks, probabilistic graphical models BibRef
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications,
PIEEE(109), No. 3, March 2021, pp. 247-278.
IEEE DOI 2103
Survey, Deep Learning. Deep learning, Systematics, Neural networks, Artificial intelligence, Machine learning, Unsupervised learning, neural networks BibRef
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions,
Surveys(54), No. 3, May 2021, pp. xx-yy.
DOI Link 2106
Survey, GAN. computer vision, GANs variants, GANs Survey, Image generation, GANs challenges, GANs, mode collapse, deep Generative models BibRef
The Prax Approach to Learning a Large Number of Texture Concepts,
AAAI-MLCV93(xx-yy). George Mason University. BibRef 9300
Michalski, R.S., and
Progress on Vision through Learning at George Mason University,
ARPA94(I:191-207). BibRef 9400
Progress on Vision Through Learning,
ARPA96(177-188). BibRef 9600
Bowyer, K.W.[Kevin W.],
Hall, L.O.[Lawrence O.], and
Report of the AAAI Fall Symposium on Machine Learning and Computer Vision: What, Why and How?,
ARPA94(I:727-731). BibRef 9400
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Evaluation and Analysis of Learning Techniques .