A VLSI-optimal constructive algorithm for classification problems

Intelligent Engineering Systems Through Artificial Neural Networks

Volumn 7, Issue , 1997, Pages 145-150

  Draghici, Sorin ^a   Beiu, Valeriu ^a   Sethi, Ishwar K ^a  

^a WAYNE STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER HARDWARE; COMPUTER SIMULATION; COMPUTER SOFTWARE; CONSTRAINT THEORY; NEURAL NETWORKS; VLSI CIRCUITS;

CLASSIFICATION PROBLEMS; CONSTRAINT BASED DECOMPOSITION TRAINING ALGORITHM; VLSI OPTIMAL CONSTRUCTIVE ALGORITHM;

PROBLEM SOLVING;

EID: 0031295420     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (34)

1. Abu-Mostafa Y.S., Connectivity Versus Entropy, Neural Information Processing Systems, D.Z. Anderson (ed.), Amer. Inst. of Physics, NY, pp. 1-8, 1988.
2. Ayestaran H.E., R.W. Prager, The Logical Gates Growing Network, TR 137, Cambridge Univ., Eng. Dept., July 1993.
3. Beiu, V., Entropy bounds for classification algorithms, Neural Network World, Vol. 6, No. 4, pp. 497-505, 1996.
4. Beiu V.- Constant Fan-In Discrete Neural Networks Are VLSI-Optimal, in S.W. Ellacott, et.al. (eds.): "Mathematics of Neural Networks: Models, Algorithms and Applications", Kluwer, 1997.
5. Beiu, V., T. De Pauw, Tight bounds on the size of neural networks for classification problems, in Proc. of Intl. Work-Conf. on Artif. and Natural NN, IWANN97, Lanzarote, June 4-6, 1997 (a).
6. Beiu V. - VLSI Complexity of Discrete Neural Networks, Gordon & Breach, 1997 (b).
7. Bose N.K., A.K. Garga, Neural Network Design Using Voronoi Diagrams, IEEE Trans. on NN's, 4 (5), 778-787, 1993.
8. Bruck J., Goodman J.W., On the power of neural networks for solving hard problems, J. of Complexity, 6, 129-135, 1990.
9. Coggins R., M. Jabri, Wattle: A Trainable Gain Analogue VLSI Neural Network, Advances in NIPS 6 (NIPS*93, Denver, CO), Morgan Kaufman, San Mateo, CA, 874-881, 1994.
10. Denker J.S., Wittner B.S., Network Generality, Training Required and Precision Required, NIPS'88, D.Z. Anderson (Ed.), Amer. Inst of Phys., New York, 219-222, 1988.
11. Draghici S. - The Constraint Based Decomposition Training Architecture, Proc. of World Congress on NN's, San Diego, Vol. III, pp. 545-555, 1994.
12. Draghici S. - Enhancements of the Constraint Based Decomposition Training Architecture, Proc. of the Intl. Conf. on NN, Washington, 3-6 June, Vol. I, pp. 317-322, 1996 (a).
13. Draghici S. - Improving the speed of some constructive algorithms by using a locking detection mechanism, Neural Network World, Vol. 6, No, 4, pp. 563-575, 1996 (b).
14. Draghici S., Sethi, I.K, On the possibilities of the limited precision weights neural networks in classification problems, Proc. of Intl. Work-Conf. on Artificial and Natural Neural Networks IWANN'97, Lanzarote, June 4-6, p. 753-762, 1997.
15. Draghici S., Beiu V., Entropy based comparison of neural networks for classification, to appear in Proc. of The 9-th Italian Workshop on Neural Nets, WIRN Vietri-sul-mare, 22-24 May, 1997 (a).
16. Draghici S., Sethi, I.K, Choosing the appropriate size network for low-cost hardware implementation: a tight lower bound for the number of bits of integer limited weights neural nets, Proc. of CSCS 11, Bucharest, Romania, 28-31 May, 1997 (b).
17. Draghici S., Sethi I.K. - Adapting theoretical constructive algorithms to hardware implementations for classification problems, to appear in Proc. of the Intl. Conf. on Eng. Appls. of Neural Networks, Stockholm, Sweden, 16-18 June, 1997 (c).
18. Dundar G., K. Rose, The Effect of Quantization on Multilayer Neural Networks, IEEE Transactions on Neural Networks 6(6), pp. 1446-1451, 1995.
19. Hammerstrom D. - The Connectivity Analysis of Simple Associations -or- How Many Connections Do We Need ?, NIPS., D.Z. Anderson (ed.), Amer. Inst. of Physics, NY, pp. 338-347, 1988.
20. Höhfeld M., S.E. Fahlman, Learning with limited numerical precision using the Cascade-Correlation Algorithm, IEEE Transactions on Neural Networks, NN-3(4), 602-611, 1992.
21. Khan A.H., E.L.Hines, Integer-weights neural nets, Elect.Lettrs, 30(15), 1237-1238, 1994.
22. Khan A.H., R.G. Wilson, Integer-weight approximation of continuous-weight multilayer feedforward nets., Proc. of the IEEE Intl. Conf. on NNs, Vol. 1, pp. 392-397, IEEE Press, NY, 1996.
23. Klaggers H. and M. Soegtrop - Limited Fan-In Random Wired Cascade-Correlation, In D.J. Myers and A.F. Murray (eds.): MicroNeuro'93, UnivEd Tech. Ltd., Edinburgh, pp. 79-82, 1993.
24. Kwan H.K., Tang C.Z., Designing Multilayer Feedforward Neural Networks Using Simplified Activation Functions and One-Power-of-Two Weights. Elec. Lett., 28(25), 2343-2344, 1992.
25. Kwan H.K., Tang C.Z., Multiplierless Multilayer Feedforward Neural Networks Design Suitable for Continuous Input-Output Mapping, Electronic Letters, 29(14), pp. 1259-1260, 1993.
26. Lang K. J, M. J. Witbrock - Learning to tell two spirals apart, Proceedings of the 1988 Connectionist Models Summer School, Morgan Kaufmann, 1988.
27. Marchesi M., G. Orlandi, F. Piazza, L. Pollonara, A. Uncini, Multilayer Perceptrons with Discrete Weights, Proc. Int. Joint Conf. on Neural Networks, San Diego, Vol. II, pp. 623-630, 1990.
28. Marchesi M., G. Orlandi, F. Piazza, A. Uncini, Fast Neural Networks without Multipliers, IEEE Transactions on Neural Networks, NN-4(1), pp. 53-62, 1993.
29. Phatak D.S. and I. Koren Connectivity and Performance Tradeoffs in the Cascade-Corelation Learning Architecture, IEEE Trans. on NN, 5(6), pp. 930-935, 1994.
30. Sethi I., Entropy nets: From decision trees to NN's, Proc. of IEEE, 1605-1613, 1990.
31. Tang C.Z., H.K. Kwan, Multilayer Feedforward Neural Networks with Single Power-of-Two Weights. IEEE Trans. On Signal Processing, SP-41(8), 2724-2727, 1993.
32. Vincent J.M., D.J.Myers, Weight Dithering and Wordlength Selection for Digital Backpropagation Networks, BT Technology J., 10(3), pp. 124-133, 1992.
33. Williamson R.C., Entropy and the complexity of feedforward neural networks, NIPS'90, R.P. Lippmann, J.E.Moody and D.S. Touretzky (Eds.), Morgan Kaufmann, San Mateo, 946-952, 1991.
34. Xie Y., M.A. Jabri, Training Algorithms for Limited Precision Feedforward Neural Networks, SEDAL TR 1991-8-3, School of EE, University of Sydney, Australia, 1991.