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On the Optimum Architecture of the Biologically Inspired Hierarchical Temporal Memory Model Applied to the Hand-Written Digit Recognition Cover

On the Optimum Architecture of the Biologically Inspired Hierarchical Temporal Memory Model Applied to the Hand-Written Digit Recognition

By: Svorad Štolc and  Ivan Bajla  
Open Access
|May 2010

References

  1. Felleman, D., van Essen, D. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex (1), 1-47.10.1093/cercor/1.1.1
  2. Serre, T., Oliva, A., Poggio, T. (2007). A feedforward architecture accounts for rapid categorization. In: Proc. National Academy of Sciences of the USA, Vol. 15. pp. 6424-6429.
  3. Lee, T. S., Mumford, D. (2003). Hierarchical Bayesian inference in visual cortex. Journal of Optical Society of America A 20(7), 1434-1448.10.1364/JOSAA.20.001434
  4. Dean, T. (2006). Scalable inference in hierarchical generative models. In: Proc. 9th Int. Symp. on Artificial Intelligence and mathematics. pp. 1-9.
  5. Hawkins, J., Blakeslee, S. (2004). On intelligence. Henry Holt and Company, New York.
  6. George, D., Hawkins, J. (2009). Towards a mathematical theory of cortical micro-circuits. PLoS Computational Biology 5(10). DOI 10.1371/journal.pcbi.1000532.10.1371/journal.pcbi.1000532274921819816557
  7. George, D., Hawkins, J. (2005). Hierarchical Bayesian model of invariant pattern recognition in the visual cortex. In: Proc. Int. Joint Conf. on Neural Networks. Montreal, Canada.
  8. Numenta (2007). Zeta1 algorithms reference. Document version 1.0.
  9. Dong, J. (2001). Statistical results of human performance on USPS database. Technical report, CEN-PARMI, Concordia University.
  10. Dong, J. (2005). HeroSvm 2.1. http://www.cenparmi.concordia.ca/~jdong/HeroSvm.html
  11. Thornton, J. R., Gustafsson, T., Blumenstein, M., Hine, T. (2006). Robust character recognition using hierarchical Bayesian network. In: Proc. 19th Australian Joint Conf. on Artificial Intelligence, Hobart, Australia. pp. 1259-1264.
  12. Thornton, J. R., Faichney, J., Blumenstein, M., Hine, T. (2008). Character recognition using hierarchical vector quantization and temporal pooling. In: Wobcke, W., Zhang, M. (eds.) Proc 21st Australasian Joint Conf. Artificial Intelligence, Vol. Lecture Notes in Computer Science. pp. 562-572.
  13. Bobier, B. (2007). Hand-written digit recognition using Hierarchical Temporal Memory. http://arts.uwaterloo.ca/~cnrglab/?q=system/files/SoftComputingFinalProject.pdf
  14. Numenta (2009). Numenta forum: benchmark with USPS handwritten digit dataset. http://www.numenta.com/phpBB2/viewtopic.php?t=224
  15. Numenta (2008). Hierarchical temporal memory, concepts, theory, and terminology. Document version 1.8.0.
  16. George, D. (2008). How the brain might work: a hierarchical and temporal model for learning and recognition. Ph.D. thesis, Dept. of Electrical Engineering, Stanford University, USA.
  17. Numenta (2009). Numenta node algorithms guide, NuPIC 1.7.
  18. Johnson, S. T. (1967). Hierarchical clustering schemes. Psychometrika 32, 241-254.10.1007/BF022895885234703
  19. Numenta (2008). Vision framework guide, NuPIC 1.6.1.
  20. Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.
  21. Wang, C. H., Srihari, S. N. (1988). A framework for object recognition in a visually complex environment and its application to locating address blocks on mail pieces. Int. Journal of Computer Vision 2(2), 125-151.10.1007/BF00133697
  22. Dong, J., Krzyzak, A., Suen, C. Y. (2001). Statistical results of human performance on USPS database. Technical report, Centre of Pattern Recognition and Machine Intelligence, Concordia University.
  23. Seewald, A. K. (2005). Digits-a dataset for hand-written digit recognition. Technical Report TR-2005-27, OFAI, Wien.
  24. Hull, J. J. (1994). A database for hand-written text recognition research. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 550-554.10.1109/34.291440
  25. LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., Jackel, L. D. (1989). Back-propagation applied to handwritten zip code recognition. Neural Computing 1(4), 541-551.10.1162/neco.1989.1.4.541
  26. Ernst, M. D. (2004). Permutation methods: A basis for exact inference. Statistical Science 19(4), 676-685. DOI 10.1214/088342304000000396.10.1214/088342304000000396
  27. Schroeder, M. R. (1991). Fractals, chaos, power laws: minutes from an infinite paradise. W. H. Freeman, New York.10.1063/1.2810323
  28. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal 27, 379-423.10.1002/j.1538-7305.1948.tb01338.x
  29. Martin, K. J., Hirschberg, D. S. (1996). Small sample statistics for classification error rates II: confidence intervals and significance tests.
Language: English
Page range: 28 - 49
Published on: May 14, 2010
Published by: Slovak Academy of Sciences, Institute of Measurement Science
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2010 Svorad Štolc, Ivan Bajla, published by Slovak Academy of Sciences, Institute of Measurement Science
This work is licensed under the Creative Commons License.

Volume 10 (2010): Issue 2 (April 2010)