- 1.J. Amsterdam, Extending the Valiant learning model, Proc. o.f 5th Int. Con}. on Machine Learning, June 12-14, 1988, pp. 381-394.Google Scholar
- 2.A. Blumer, A. Ehrenfeucht, D. Haussler, and M.K. Warmuth, Learnability and the Vapnik- Chervonenkis dimension, J. Assoc. Comput. Mach., vol 36, no.4, 1989, pp. 929-965. Google ScholarDigital Library
- 3.R.S. Boyer, and J.S. Moore, A fast string searching algorithm, Communications of ACM, vol. 20, 1977, pp. 762~772. Google ScholarDigital Library
- 4.H. Bunke, H., Hybrid Approaches, in Syntactic and Structural Pattern Recognition, Edited by G. Ferrate, T. Pavlidis, A. Sanfeliu, H. Bunke, N ATO ASI Series, Series F: Computer and System Sciences Vol. 45, Springer-Verlag, 1988, pp. 335-361. Also private communication in June 1990. Google ScholarDigital Library
- 5.R.E. Duds, and P.E. Hart, Pattern Classification and Scene Analysis, Wiley, New York, 1973.Google Scholar
- 6.H.F. Durrant-Whyte, Consistent integration and propagation of disparate sensor observations, Int. J. Robot. Res., vol. 6, no.3, 1987, pp. 3-24. Google ScholarDigital Library
- 7.C.W. Glover, M. Silliman, M. Walker, P. Spelt, N.S.V. Rao, Hybrid neurad network and rulebased pattern recognition system capable of selfmodification, Proc. SPiE Conf. on Applications o.f Artificial Intelligence, vol. VIII, ed. M. Trivedi, 1990.Google Scholar
- 8.D. Haussler, Generalizing the PAC model: sample size bounds from metric dimension-based uniform convergence results, Proc. 3rd Syrup. Found. Comput. Sci., 1989, pp. 40-45.Google Scholar
- 9.D.S. Hirschberg, A linear space algorithm for computing maximal common subsequences, Commun. ACM, vol. 18, no. 6, 1975, 341-343. Google ScholarDigital Library
- 10.A. Kak and S. Chen (eds.), Spatial Reasonin# and Multi-Sensor Fusion, Morgan Kaufman Pub. Inc., 1987. Google ScholarDigital Library
- 11.D.E. Knuth, J.H. Marris and V.R. Pratt, Fast pattern matching in strings, SIAM J. Computing, vol. 6, 1977, pp. 323-250.Google ScholarCross Ref
- 12.G.M. Landau and U. Vishkin, Fast parallel and serial approximate string matching, J. Algorithms, vol. 10, 1989, pp. 157-169. Google ScholarDigital Library
- 13.R.C. Luo and M.G. Kay, Multisensor integration and fusion in intelligent systems, IEEE Trans. Syst. Man Cybernetics, vol. 19, no. 5, 1989, pp. 901-931.Google ScholarCross Ref
- 14.M.L. Minsky and S. A. Papert, Perceptrons, expanded edition, MIT Press, 1988. Google ScholarDigital Library
- 15.B.K. Natarajan, On learning sets and functions, Machine Learning, vol.4, 1989, pp. 67-97. Google ScholarDigital Library
- 16.Y. Pao, Adaptive Pattern Recognition and Neural Networks, Addison-Wesley Pub. Co., 1989. Google ScholarDigital Library
- 17.N.S.V. Rao and C.W. Glover, Computational aspects of similarity measures based on linearizations of lattice points, Proc. IAPR Workshop on Syn. tactic and Structural Pattern Recognition, Murray Hill, New Jersey, June 13-15, 1990, pp. 351-365.Google Scholar
- 18.N.S.V. Rao, E.M. Oblow, C.W. Glover and G.E. Liepins, N-learners Problem: Fusion of concepts, Proc. Int. Conf. on Intelligent Robots and Systems, RMeigh, NC, 1992, pp. 1372-1380, full version to appear in iEEE Transactions on Systems, Man and Cybernetics.Google ScholarCross Ref
- 19.N.S.V. Rao, W. Wu, and C.W. Glover, Algorithms for recognizing planar polygonal configurations using perspective images, IEEE Trans. Robotics and Automation, vol. 8, No. 4, pp. 480-485.Google ScholarCross Ref
- 20.A. R. Reibman and L.W. Nolte, Optimal detection and performance of distributed sensor systems, IEEE Trans. Aerospace Electronic Syst., vol. AES- 23, no.l, 1987, pp. 24-30.Google ScholarCross Ref
- 21.D.E. Rumelhart et al, Parallel Distributed Processing, Vol.1, MIT Press, 1986. Google ScholarDigital Library
- 22.M.R. Teague, image analysis via the general theory of moments, J. Opt. Soc. Am., vol. 70, 1987, pp. 920-930.Google ScholarCross Ref
- 23.L.G. Valiant, A Theory of the learnable, Communications o! the A CM, vol. 27, 1984, pp. 1134-1142. Google ScholarDigital Library
- 24.V.N. Vapnik, Estimation of Dependences Based on Empirical Data, Springer-Verlag, New York, 1982. Google ScholarDigital Library
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