Skip to main content
  • 64 Accesses

Abstract

Chapter 4 ties the rapid learning neuro-computing system to related neural network, mental process, and statistical estimation models. Section 4.1 presents key rapid learning neuro-computing system components. Section 4.2 describes a corresponding biological neural network model, section 4.3 describes a closely related mental process model, and section 4.4 describes a corresponding statistical model.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. R.J. Jannarone, “Conjunctive Item Response Theory: Cognitive Research Prospects,” in M. Wilson (Ed), Objective Measurement: Theory into Practice, Vol.1, Ablex, Norwood, NJ, pp. 211 – 236, 1992.

    Google Scholar 

  2. R.J. Jannarone, “A Concurrent Learning and Performance Information Processing System,” RCNS Technical Report Series, No. PAT94-01, Rapid Clip Neural Systems, Inc., Atlanta, GA, 1994.

    Google Scholar 

  3. R.J. Jannarone, “Concurrent Learning and Performance Analog Chip Design,” RCNS Technical Report Series, No. PAT96-01, Rapid Clip Neural Systems, Inc., Atlanta, GA, 1996.

    Google Scholar 

  4. C. Mead, Analog VLSI and Neural Systems, Addison-Wesley, Reading, MA, 1995.

    Google Scholar 

  5. C. Mead and L. Conway, Introduction to VLSI Systems, Addison-Wesley, Reading, MA, 1980.

    Google Scholar 

  6. K. Naik, A Concurrent Information Processing Parallel Design Analysis, Unpublished Masters Thesis, University of South Carolina, 1996.

    Google Scholar 

  7. J. A. Deutsch & D. Deutch, Physiology Psychology, Dorsey, Homewood, IL, 1973.

    Google Scholar 

  8. D.E. Rumelhert & W.L. McClelland (Eds), Parallel Distributed Processing, Explorations in the Microstructure of Cognition, Vol. 1, MIT Press, Cambridge, MA, 1986.

    Google Scholar 

  9. P. Wasserman, Advanced Methods in Neural Computing, Van Nostrand Re-inhold, New York, 1993.

    MATH  Google Scholar 

  10. H. Gleitman, Basic Psychology, W W. Norton, New York, 1983.

    Google Scholar 

  11. G. Tatman, R.J. Jannarone, & C M. Amick, “Neural Networks for Speech Recognition: Contrasts Between a Traditional and a Parametric Approach,” Unpublished Technical Report, Machine Cognition Laboratory, University of South Carolina, 1994.

    Google Scholar 

  12. R.D. Hawkins, T. Abrams, T.J. Carew, & E.R. Kandel, “A Cellular Mechanism of Classical Conditioning in Aplysia: Activity-Dependent Amplification of Presynaptic Facilitation,” Science, Vol. 219, pp. 400 – 405, 1983.

    Article  Google Scholar 

  13. S. Coren, C. Porac, & L.M. Ward, Sensation and Perception, Academic Press, New York, 1978.

    Google Scholar 

  14. J.M. Mendel, Lessons in Estimation Theory for Signal Processing, Communications and Control, Prentice-Hall PTR, Englewood Cliffs, NJ, 1995.

    MATH  Google Scholar 

  15. E.L. Lehmann, Theory of Point Estimation, Wiley, New York, 1983.

    MATH  Google Scholar 

  16. E.L. Lehmann, Testing Statistical Hypotheses, 2nd Edn., Wiley, New York, 1986.

    MATH  Google Scholar 

  17. P.J. Bickel & K.A. Doksum, Mathematical Statistics: Basic Ideas and Selected Topics, Holden-Day, San Francisco, 1977.

    MATH  Google Scholar 

  18. R.J. Jannarone,“The ABC of Measurement,” Unpublished Technical Report, Machine Cognition Laboratory, University of South Carolina, 1994.

    Google Scholar 

  19. T.W. Anderson, An Introduction to Multivariate Statistical Analysis, 2nd Edn., Wiley, New York, 1984.

    MATH  Google Scholar 

  20. H. Scheffé, The Analysis of Variance, Wiley, New York, 1983.

    Google Scholar 

  21. D.R. Cox, The Analysis of Binary Data, London, Methuen, 1966.

    Google Scholar 

  22. Y.M.M. Bishop, S.E. Fienberg, & P.W. Holland, Discrete Multivariate Analysis: Theory and Practice, MIT Press, Cambridge, MA, 1975.

    MATH  Google Scholar 

  23. L.A. Goodman, Analyzing Qualitative/Categorical Data, Jay Magidson, Cambridge, MA, 1978.

    MATH  Google Scholar 

  24. R. J. Jannarone, K.F. Yu, and Y. Takefuji, “Conjunctoids: Statistical Learning Modules for Binary Events,” Neural Networks, Vol. 1, pp. 325 – 337, 1988.

    Article  Google Scholar 

  25. R.J. Jannarone, “Conjunctive Item Response Theory Kernels,” Psychometrika, Vol. 51, pp. 449 – 460, 1986.

    Article  Google Scholar 

  26. R.J. Jannarone, “Locally dependent models: conjunctive item response theory,” In W.J. van der Linden & R.K. Hambleton III (Eds.), Handbook of Modern Item Response Theory, Springer-Verlag, New York, pp. 465 – 480, 1996.

    Google Scholar 

  27. R.J. Jannarone, K.F. Yu, and J.E. Laughlin, “Easy Bayes Estimation for Rasch Type Models,” Psychometrika, Vol. 55, pp. 449 – 460, 1990.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Chapman & Hall

About this chapter

Cite this chapter

Jannarone, R.J. (1997). Rapid Learning Models. In: Concurrent Learning and Information Processing. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0431-9_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-4613-0431-9_4

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-8049-8

  • Online ISBN: 978-1-4613-0431-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics