Deep structure learning: Beyond connectionist approaches

Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012

Volumn 1, Issue , 2012, Pages 162-167

  Mitchell, Ben ^a   Sheppard, John ^b  

^a Johns Hopkins University   (United States)

^b Idaho National Laboratory   (United States)

Author keywords

deep architecture; deep learning; feature extraction; image classification; object recognition

Indexed keywords

CLASSIFICATION PERFORMANCE; CONNECTIONIST APPROACH; CONNECTIONIST MODELS; DEEP ARCHITECTURES; DEEP LEARNING; DEEP STRUCTURE; DIMENSIONALITY REDUCTION TECHNIQUES;

FEATURE EXTRACTION; IMAGE CLASSIFICATION; LEARNING SYSTEMS; OBJECT RECOGNITION;

ARCHITECTURE;

EID: 84873588393     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2012.34     Document Type: Conference Paper

References (17)

1. E. Adelson, C. Anderson, J. Bergen, P. Burt, and J. Ogden. PyramidMethods in Image Processing. RCA Engineer, 29-6, 1984.
2. Ethem Alpaydin. Introduction to Machine Learning. MIT Press, 2004.
3. M. Andrecut. Parallel gpu implementation of iterative pca algorithms.Journal of Computational Biology, 16(11), 2008.
4. S. Behnke. Hierarchical Neural Networks for Image Interpretation. Springer, 2003.
5. Y. Bengio. Learning deep architectures for AI. Foundations and Trendsin Machine Learning, 2(1):1-127, 2009. Also published as a book. NowPublishers, 2009.
6. Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle. Greedy layerwisetraining of deep belief networks. Advances in Neural InformationProcessing Systeml 19 (NIPS '06), pages 153-160, 2007.
7. Y. Bengio and Y. LeCun. Scaling learning algorithms towards AI. Large-Scale Kernel Machines, 2007.
8. D. Erhan, Y. Bengio, A. Courville, P. Manzagol, and P. Vincent. Whydoes unsupervised pre-training help deep learning? Journal of MachineLearning Research, 11:625-660, 2010.
9. K. Fukushima. Neocognitron: A self-organizing neural network modelfor a mechanism of pattern recognition unaffected by shift in position.Biological Cybernetics, 36(4):193-202, 1980.
10. D. George and J. Hawkins. Invariant Pattern Recognition using BayesianInference on Hierarchical Sequences. Proc. of International JointConference on Nerual Networks, 2005.
11. J. Hawkins and S. Blakeslee. On Intelligence. Owl Books, Henry Holtand Company, 2004.
12. G. E. Hinton, S. Osindero, and Y. W. Teh. A fast learning algortihm fordeep belief nets. Neural Computation, 18:1527-1554, 2006.
13. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learningapplied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
14. T. Mitchell. The need for biases in learning generalizations. TechnicalReport CBM-TR-117, 1980.
15. C. Shaw and J. McEachern. Toward a theory of neuroplasticity.Psychology Press, 2001.
16. M. Turk and A. Pentland. Face recognition using eigenfaces. Proc.IEEE Conference on Computer Vision and Pattern Recognition, pages586-591, 1991.
17. D. H. Wolpert and W. G. Macready. No free lunch theorems for search.Technical Report SFI-TR-95-02-010, 1995.