Open Access
Issue
SHS Web Conf.
Volume 75, 2020
The International Conference on History, Theory and Methodology of Learning (ICHTML 2020)
Article Number 04018
Number of page(s) 14
Section Methodology of Learning, Education and Training
DOI https://doi.org/10.1051/shsconf/20207504018
Published online 26 March 2020
  1. H. Abelson, G.J. Sussman, J. Sussman, Structure and Interpretation of Computer Programs, 2nd edn. (MIT Press, Cambridge, 1996) [Google Scholar]
  2. T.H. Abraham, (Physio)logical circuits: The intellectual origins of the McCulloch-Pitts neural networks. Journal of the History of the Behavioral Sciences. 38 (1),3–25 (2002). doi:10.1002/jhbs.1094 [Google Scholar]
  3. E. Anderson, The Species Problem in Iris. Annals of the Missouri Botanical Garden. 23(3), 457¬469+471-483+485-501+503-509 (1936). doi:10.2307/2394164. [Google Scholar]
  4. E. Anderson, Plants, Man and Life (University of California Press, Boston, 1952) [Google Scholar]
  5. E. Anderson, Bulletin of the American Iris Society. 59, 2–5 (1935) [Google Scholar]
  6. E. Anderson, The Problem of Species in the Northern Blue Flags, Iris versicolor L. and Iris virginica L. Annals of the Missouri Botanical Garden. 15(3),241–332 (1928). doi:10.2307/2394087 [Google Scholar]
  7. A.S. Ayed, Master thesis, Memorial University, 1997 [Google Scholar]
  8. J.J. Buergermeister, in Restructuring Training and Education through Technology, ed. by D.W. Dalton. 32nd Annual Conference of the Association for the Development of Computer-Based Instructional Systems, San Diego, California, October 29- November 1, 1990. (ADCIS International, Columbus, 1990), pp. 214–220 [Google Scholar]
  9. H. Chernoff, Journal of the American Statistical Association. 68(342),361–368 (1973) [Google Scholar]
  10. J.D. Cowan in Talking nets: An oral history of neural networks, ed. by J.A. Anderson, E. Rosenfeld (MIT Press, Cambridge, 1998), pp. 97–124 [Google Scholar]
  11. P. Cull, The mathematical biophysics of Nicolas Rashevsky. BioSystems. 88 (3),178–184 (2007). doi: 10.1016/j.biosystems.2006.11.003 [Google Scholar]
  12. R.C. Eberhart, R.W. Dobbins, in Neural Network PC Tools: A Practical Guide, ed. by R.C. Eberhart, R.W. Dobbins (Academic Press, San Diego, 1990), pp. 9¬34 [Google Scholar]
  13. R.A. Fisher, The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics. 7 (2),179–188 (1936). doi: 10.1111/j.1469-1809.1936.tb02137.x [Google Scholar]
  14. R.S. Freedman, R.P. Frail, F.T. Schneider, B. Schnitta, in Proceedings First International Conference on Artificial Intelligence Applications on Wall Street, Institute of Electrical and Electronics Engineers, New York, 9–11 Oct. 1991 [Google Scholar]
  15. T. Hegazy, A. Ayed, Neural Network Model for Parametric Cost Estimation of Highway Projects. Journal of Construction Engineering and Management. 124 (3),210–218 (1998). doi:10.1061/(ASCE)0733-9364(1998)124:3(210) [Google Scholar]
  16. T.T. Hewett, Teaching Students to Model Neural Circuits and Neural Networks Using an Electronic Spreadsheet Simulator. Behavior Research Methods, Instruments, & Computers. 17 (2),339–344 (1985). doi:10.3758/BF03214406 [Google Scholar]
  17. T.T. Hewett, Using an Electronic Spreadsheet Simulator to Teach Neural Modeling of Visual Phenomena. (Drexel University, Philadelphia, 1985) [Google Scholar]
  18. A.S. Householder, H.D. Landahl, Mathematical Biophysics of the Central Nervous System (Principia Press, Bloomington, 1945) [Google Scholar]
  19. A.S. Householder, A neural mechanism for discrimination: II. Discrimination of weights. Bulletin of Mathematical Biophysics. 2(1),1–13 (1940). doi: 10.1007/BF02478027 [Google Scholar]
  20. A.S. Householder, A theory of steady-state activity in nerve-fiber networks I: Definitions and Preliminary Lemmas. Bulletin of Mathematical Biophysics. 3(2),63–69 (1941). doi: 10.1007/BF02478220 [Google Scholar]
  21. W. James, Psychology (Henry Holt and Company, New York, 1892) [Google Scholar]
  22. W. James, The Principles of Psychology (Henry Holt and Company, New York, 1890) [Google Scholar]
  23. S.J. Johnston, InfoWorld. 13(7), 14 (1991) [Google Scholar]
  24. D.A. Kendrick, P.R. Mercado, H.M. Amman, Computational Economics (Princeton University Press, Princeton, 2006) [CrossRef] [Google Scholar]
  25. H.D. Landahl, W.S. McCulloch, W. Pitts, A statistical consequence of the logical calculus of nervous nets. Bulletin of Mathematical Biophysics. 5 (4),135–137 (1943). doi:10.1007/BF02478260 [Google Scholar]
  26. H.D. Landahl, R. Runge, Outline of a matrix calculus for neural nets. Bulletin of Mathematical Biophysics. 8 (2),75–81 (1946). doi:10.1007/BF02478464 [Google Scholar]
  27. H.D. Landahl, A matrix calculus for neural nets: II. Bulletin of Mathematical Biophysics. 9 (2),99–108 (1947). doi: 10.1007/BF02478296 [Google Scholar]
  28. O. Markova, S. Semerikov, M. Popel, CoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. (CEUR Workshop Proceedings, 2018), http://ceur-ws.org/Vol-2104/paper_204.pdf. Accessed 30 Nov 2018 [Google Scholar]
  29. O.M. Markova, S.O. Semerikov, A.M. Striuk, H.M. Shalatska, P.P. Nechypurenko, V.V. Tron, Implementation of cloud service models in training of future information technology specialists. (CEUR Workshop Proceedings, 2019), http://ceur- ws.org/Vol-2433/paper34.pdf. Accessed 10 Sep 2019 [Google Scholar]
  30. W.C. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics. 5(4),115–133 (1943). doi: 10.1007/BF02478259 [CrossRef] [MathSciNet] [Google Scholar]
  31. T.M. Mitchell, Key Ideas in Machine Learning. http://www.cs.cmu.edu/%7Etom/mlbook/keyIdeas.p df. Accessed 28 Jan 2019 [Google Scholar]
  32. O.S. Permiakova, S.O. Semerikov, Zastosuvannia neironnykh merezh u zadachakh prohnozuvannia (The use of neural networks in forecasting problems), in Materials of the International Scientific and Practical Conference “Young scientist of the XXI century”, KTU, Kryviy Rih, 17-18 November 2008 [Google Scholar]
  33. W. Pitts, W.S. McCulloch, How we know universals the perception of auditory and visual forms. Bulletin of Mathematical Biophysics. 9 (3),127–147 (1947). doi: 10.1007/BF02478291 [Google Scholar]
  34. W. Pitts, A general theory of learning and conditioning: Part I. Psychometrika. 8(1),1–18 (1943). doi: 10.1007/BF02288680 [Google Scholar]
  35. W. Pitts, A general theory of learning and conditioning: Part II. Psychometrika. 8(2),131–140 (1943). doi: 10.1007/BF02288697 [Google Scholar]
  36. W. Pitts, Some observations on the simple neuron circuit. Bulletin of Mathematical Biophysics. 4 (3),121–129 (1942). doi: 10.1007/BF02477942 [Google Scholar]
  37. W. Pitts, The linear theory of neuron networks: The dynamic problem. Bulletin of Mathematical Biophysics. 5 (1),23–31 (1943). doi: 10.1007/BF02478116 [Google Scholar]
  38. W. Pitts, The linear theory of neuron networks: The static problem. Bulletin of Mathematical Biophysics. 4 (4),169–175 (1942). doi:10.1007/BF02478112 [Google Scholar]
  39. N. Rashevsky, Mathematical biophysics of abstraction and logical thinking. Bulletin of Mathematical Biophysics. 7 (3),133–148 (1945). doi: 10.1007/BF02478314 [Google Scholar]
  40. N. Rashevsky, Outline of a physico-mathematical theory of excitation and inhibition. Protoplasma. 20 (1),42–56 (1933). doi:10.1007/BF02674811 [Google Scholar]
  41. N. Rashevsky, Some remarks on the boolean algebra of nervous nets in mathematical biophysics. Bulletin of Mathematical Biophysics. 7 (4),203–211 (1945). doi: 10.1007/BF02478425 [Google Scholar]
  42. N. Rashevsky, The neural mechanism of logical thinking. Bulletin of Mathematical Biophysics. 8(1),29–40 (1946). doi: 10.1007/BF02478425 [Google Scholar]
  43. T.F. Rienzo, K.K. Athappilly, Introducing Artificial Neural Networks through a Spread-sheet Model. Decision Sciences Journal of Innovative Education. 10(4),515–520 (2012). doi:10.1111/j.1540-4609.2012.00363.x [Google Scholar]
  44. M.A. Ruggiero, Cybernetic Trading Strategies: Developing a Profitable Trading System with State- of-the-Art Technologies (John Wiley & Sons, New York, 1997) [Google Scholar]
  45. M. Ruggiero, US Patent 5,241,620, 31 Aug 1993 [Google Scholar]
  46. K. Schwab, N. Davis, Shaping the Fourth Industrial Revolution (Portfolio Penguin, London, 2018) [Google Scholar]
  47. S.O. Semerikov, I.O. Teplytskyi, Yu.V. Yechkalo, A.E. Kiv, Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. (CEUR Work-shop Proceedings, 2018), http://ceur-ws.org/Vol-2257/paper14.pdf. Accessed 21 Mar 2019 [Google Scholar]
  48. S.O. Semerikov, I.O. Teplytskyi, Metodyka uvedennia osnov Machine learning u shkilnomu kursi informatyky (Methods of introducing the basics of Machine learning in the school course of informatics), in Problems of informatization of the educational process in institutions of general secondary and higher education. Ukrainian scientific and practical conference, Kyiv, October 09, 2018. (Vyd-vo NPU imeni M. P. Drahomanova, Kyiv, 2018), pp. 18–20 [Google Scholar]
  49. A. Shimbel, A. Rapoport, A statistical approach to the theory of the central nervous system. Bulletin of Mathematical Biophysics. 10(2),41–55 (1948). doi: 10.1007/BF02478329 [Google Scholar]
  50. G.L. Stebbins, Edgar Anderson 1897-1969. (National Academy of Sciences, Washington, 1978) [Google Scholar]
  51. G.J. Sussman, J. Wisdom, Structure and interpretation of classical mechanics, 2nd edn. (MIT Press, Cambridge, 2015) [Google Scholar]
  52. I.O. Teplytskyi, O.I. Teplytskyi, A.P. Humeniuk, New computer technology. 6, 67–68 (2008) [Google Scholar]
  53. I.O. Teplytskyi, Elementy kompiuternoho modeliuvannia (Elements of computer simulation), 2nd edn. (KSPU, Kryvyi Rih, 2010) [Google Scholar]
  54. T. Wei, On matrices of neural nets. Bulletin of Mathematical Biophysics. 10 (2),63–67 (1948). doi: 10.1007/BF02477433 [Google Scholar]
  55. P.J. Werbos, Maximizing long-term gas industry profits in two minutes in Lotus using neural network methods. Transactions on Systems Man and Cybernetics. 19 (2),315–333 (1989). doi: 10.1109/21.31036 [Google Scholar]
  56. G. Young, On reinforcement and interference between stimuli. Bulletin of Mathematical Biophysics. 3(1),5–12 (1941). doi: 10.1007/BF02478102 [Google Scholar]
  57. T. Zaremba, in Neural Network PC Tools: A Practical Guide, ed. by R.C. Eberhart, R.W. Dobbins (Academic Press, San Diego, 1990), pp. 251–283 [Google Scholar]

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