14.5.2 Learning, General Surveys, Overviews

Chapter Contents (Back)
Survey, Learning. Learning.

Schwartz, S.R., 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

Bhanu, B., Poggio, T.,
Introduction to the Special Section on Learning in Computer Vision,
PAMI(16), No. 9, September 1994, pp. 865-867.
IEEE Top Reference. BibRef 9409

Bhanu, B., Peng, J., Huang, T., Draper, B.,
Introduction to the Special Issue on Learning in Computer Vision and Pattern Recognition,
SMC-B(35), No. 3, June 2005, pp. 391-396.

Vapnik, V.,
The Nature of Statistical Learning Theory,
Springer-Verlag1996. BibRef 9600

Vapnik, V.,
Statistical Learning Theory,
John Wiley& Sons, 1998. BibRef 9800

Vapnik, V.[Vladimir],
An overview of statistical learning theory,
TNN(10), No. 5, 1999, pp. 988-999. 0906

Dietterich, T.G.,
Machine Learning Research: Four Current Directions,
AI Magazine(18), No. 4, 1997, pp. 97-136. BibRef 9700

Poggio, T.[Tomaso], 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

Petrou, M.[Maria],
Learning in Pattern Recognition: Some Thoughts,
PRL(22), No. 1, January 2001, pp. 3-13.
Elsevier DOI 0105

Petrou, M.[Maria],
Learning in Computer Vision: Some Thoughts,
Springer DOI 0711

Xu, M.[Mai], Petrou, M.[Maria],
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,
ACCV09(III: 341-350).
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

Xu, M.[Mai], Wang, Z.[Zulin], Petrou, M.[Maria],
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.], Perona, P.[Pietro], Schölkopf, B.[Bernhard],
Guest Editorial Machine Learning for Vision,
IJCV(77), No. 1-3, May 2008, pp. 1.
Springer DOI 0803

Raducanu, B.[Bogdan], Vitria, J.[Jordi],
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,
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

Raducanu, B.[Bogdan], Vitria, J.[Jordi], Leonardis, A.[Ales],
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

Darrell, T.J., Lampert, C., Sebe, N., Wu, Y., Yan, Y.,
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.
Collaboration, Computer vision, Information sharing, Learning systems, Machine learning, Multimedia communication, Training data BibRef

Nagy, G.[George],
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).

Wang, X.H.[Xiao-Han], Eliott, F.M.[Fernanda M.], Ainooson, J.[James], Palmer, J.H.[Joshua H.], Kunda, M.[Maithilee],
An Object is Worth Six Thousand Pictures: The Egocentric, Manual, Multi-image (EMMI) Dataset,
WWW Link. 1802
Dataset, Learning. Egocentric, Manual, Multi-Image (EMMI) Dataset. Automobiles, Cameras, Manuals, Object recognition, Toy manufacturing industry, Training, Visualization BibRef

Bala, J.W., Michalski, R.S., Wnek, J.,
The Prax Approach to Learning a Large Number of Texture Concepts,
AAAI-MLCV93(xx-yy). George Mason University. BibRef 9300

Bala, J.W., Michalski, R.S., and Pachowicz, P.W.,
Progress on Vision through Learning at George Mason University,
ARPA94(I:191-207). BibRef 9400

Michalski, R.S., Rosenfeld, A., Aloimonos, Y., Duric, Z., Maloof, M.A., Zhang, Q.,
Progress on Vision Through Learning,
ARPA96(177-188). BibRef 9600

Bhanu, B.[Bir], Bowyer, K.W.[Kevin W.], Hall, L.O.[Lawrence O.], and Langley, P.[Pat],
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 .

Last update:May 10, 2021 at 18:51:10