Implementing probabilistic neural networks

Neural Computing and Applications

Volumn 5, Issue 3, 1997, Pages 152-159

  Ancona, Fabio ^a   Colla, Anna Maria ^b   Rovetta, Stefano ^a   Zunino, Rodolfo ^a  

^a UNIVERSITY OF GENOA   (Italy)

^b Elsag Bailey Telerobot   (Italy)

Author keywords

Digital neural processor; Generalisation; Hardware implementation; Probabilistic neural networks; Random optimisation

Indexed keywords

EID: 21744457566     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/BF01413860     Document Type: Article

References (18)

1. Specht DF. Probabilistic neural networks. Neural Networks 1990; 3: 109-118
2. Duda RO, Hart PE. Pattern Classification and Scene Analysis, John Wiley, Chichester 1973
3. Parzen E. On estimation of a probability density function and mode. Annals of Mathematical Statistics 1962; 33: 1065-1076
4. Specht DF. Probabilistic neural networks and polynomial adaline as a complementary technique for classification. IEEE Trans Neural Networks 1990; 1: 111-121
5. Widrow B, Hoff ME. Adaptive switching circuits. IRE WESCON Convention Record, New York, 1960, 96-104
6. Burrascano P. Learning vector quantization for the probabilistic neural network. IEEE Trans Neural Networks 1991; 2(4): 458-461
7. Kohonen T. Self Organization and Associative Memories, Springer, 1982
8. Intel Corporation. Ni1000 Beta Release 2.3 documentation, 1994
9. Cacoullos T. Estimation of a multivariate density. Annals of the Institute of Statistical Mathematics (Tokyo) 1966; 18(2): 179-189
10. Moody J, Darken C. Fast learning in networks of locally-tuned processing units. Neural Computation 1989; 1: 281-294
11. Moody JE. The effective number of parameters: an analysis of generalization and regularization in nonlinear learning systems. In: Moody JE, Hanson SJ, Lippman RP (eds). NIPS 4 1991; 4: 847-854
12. Vapnik VN. The Nature of Statistical Learning, Springer-Verlag, New York, 1995
13. Wolpert DH. Off-training set error and a priori differences between learning algorithms. Technical Report SFI-TR-95-01-003, The Sante Fe Institute, Santa Fe, NM, 1995
14. Specht DF. Generation of polynomial discriminant functions for pattern recognition. IEEE Trans Electronic Computers 1967; 16: 308-319
15. Matyas J. Random optimization. Automation and Remote Control, 1965; 26: 246-253
16. Solis FJ, Wets JB. Minimization by random search techniques. Mathematics of Operations Research 1981; 6: 19-30
17. Baba N, Shoman T, Sawaragi Y. A modified convergence theorem for a random optimization method. Information Sciences 1977; 13: 159-166
18. Battiti R, Colla AM. Democracy in neural nets: Voting schemes for classification. Neural Networks 1994; 7(4): 691-707