Artificial Neural Networks for Document Analysis and Recognition

Bibliography on Artificial Neural Networks
[BC91] E. Bernard and D. Casasent. Invariance and neural nets. IEEE TNN, 2(5):498-508, 1991.
[BC94] R. Battiti and A.M. Colla. Democracy in neural nets - voting schemes for classification. Neural Networks, 7(4):691-707, 1994.
[BCG+94] J.L. Blue, G. T. Candela, P.J. Grother, R. Chellappa, and C.L. Wilson. Evaluation of pattern classifiers for fingerprint and ocr applications. Pattern Recognition, 27:485-, 1994.
[BFG95] M. Bianchini, P. Frasconi, and M. Gori. Learning in multilayered networks used as autoassociators. IEEE TNN, 6(2):512-515, 1995.
[BSB+92] B.E. Boser, E. Sackinger, J. Bromley, Y. LeCun, R.E. Howard, and L.D. Jackel. Hardware requirements for neural network pattern classifiers: A case study and implementation. In From Pixels to Features III, pages 467-478. North Holland, 1992.
[Bur88] D. J. Burr. Experiments on neural net recognition of spoken and written text. IEEE Trans. Acoustics, Speech, and Signal Processing, 36(7):1162-1168, 1988.
[CGM+92] G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen. Fuzzy ARTMAP: a neural network architecture for incremental learning of analog multidimensional maps. IEEE TNN, 3(5):698-713, 1992.
[CP03] D. Chakraborty and N.R. Pal. A novel training scheme for multilayered perceptrons to realize proper generalization and incremental learning. IEEE TNN, 14(1):1-14, 2003.
[dMGM01] C. de Mauro, M. Gori, and M. Maggini. APEX: an adaptive visual information retrieval system. In ICDAR 01, pages 898-902, 2001.
[FGKS01] P. Frasconi, M. Gori, A. Kuechler, and A. Sperduti. From sequences to data structures: Theory and applications. In J.F. Kolen and S.C. Kremer, editors, A field guide to dynamical recurrent networks. IEEE Press, New York, 2001. Chapter 22.
[FGS97] P. Frasconi, M. Gori, and G. Soda. Links between LVQ and Backpropagation. PRL, 18(4):303-310, 1997.
[FGS98] P. Frasconi, M. Gori, and A. Sperduti. A general framework for adaptive processing of data structures. IEEE TNN, 9(5):768-786, 1998.
[FGS01] P. Frasconi, M. Gori, and A. Sperduti. Guest editors' introduction: special section on connectionist models for learning in structured domains. IEEE Trans. Knowledge and Data Engineering, 13(2):145-147, 2001.
[FM82] K. Fukushima and S. Miyake. Neocognitron: a new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recognition, 15(6):455-469, 1982.
[Fu94] Li Min Fu. Neural networks in computer intelligence. McGraw-Hill, New York, NY, 1994.
[Fuk87a] K. Fukushima. A neural network model for selective attention. In First Int'l Conference on Neural Networks, pages 11-18, 1987.
[Fuk87b] K. Fukushima. A neural network model for selective attention in visual pattern recognition and associative recall. Applied Optics, 26(23):4985-4992, 1987.
[Fuk88] K. Fukushima. A neural network for visual pattern recognition. Computer, pages 65-75, March 1988.
[GAP93] A.H. Gee, S.V.B. Aiyer, and R.W. Prager. An analytical framework for optimizing neural networks. Neural Networks, 6(1):79-98, 1993.
[GB03] J. Ghosn and Y. Bengio. Bias learning, knowledge sharing. IEEE TNN, 14(4):748-765, 2003.
[GMM+03] M. Gori, M. Maggini, S. Marinai, J.Q. Sheng, and G. Soda. Edge-backpropagation for noisy logo recognition. Pattern Recognition, 36(1):103-110, 2003.
[GNB95] H.P. Graf, C.R. Nohl, and J. Ben. Image recognition with an analog neural net chip. MVA, 8(2):131, 1995.
[GS98] M. Gori and F. Scarselli. Are multilayer perceptrons adequate for pattern recognition and verification? IEEE TPAMI, 20(10):1121-1132, 1998.
[HST03] M. Hagenbuchner, A. Sperduti, and A.C. Tsoi. A self-organizing map for adaptive processing of structured data. IEEE TNN, 14(3):491-505, 2003.
[JDM00] A.K. Jain, R.P.W. Duin, and J. Mao. Statistical pattern recognition: a review. IEEE TPAMI, 22(1):4-37, January 2000.
[KJ03] R. Kothari and V. Jain. Learning from labeled and unlabeled data using a minimal number of queries. IEEE TNN, 14(6):1496-1504, 2003.
[KL90] A. Khotanzad and J.H. Lu. Classification of invariant image representations using a neural network. IEEE Trans. Acoustics, Speech, and Signal Processing, 38:1028-1038, 1990.
[Koh90] T. Kohonen. The self-organizing map. Proc. IEEE, 78(9):1464-1480, 1990.
[KP00] G.N. Karystinos and D.A. Pados. On overfitting, generalization, and randomly expanded training sets. IEEE TNN, 11(5):1050-1057, 2000.
[Lee99] Nam Il Lee. A coupled-art neural network capable of modularized categorization of patterns. PRL, 20:131-140, 1999.
[LG00] J. Liu and P. Gader. Outlier rejection with mlps and variants of RBF networks. In ICPR 00, pages 680-683, 2000.
[Lip87] R. P. Lippmann. An introduction to computing with neural nets. IEEE ASSP Magazine, 4(2):4-22, 1987.
[LS95] D.-S. Lee and S. N. Srihari. Dynamic classifier combination using neural network. In Proc. SPIE - Doc. Rec. II, pages 26-37, 1995.
[Mar96] G.L. Martin. The impact of letter classification learning in reading. In Proc. 18th Conf. of the Cognitive Science Society, 1996.
[Mit97] Tom Mitchell. Machine Learning. McGraw Hill, 1997.
[RHW86a] D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning internal representations by error propagation. In D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing, volume 1, chapter 8, pages 318-362. MIT Press, Cambridge, 1986.
[RHW86b] D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning representations by back-propagating errors. Nature, 323:533-536, 1986.
[RMtPRG86] D. E. Rumelhart, J. L. McClelland, and the PDP Research Group. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, volume 1. MIT Press, Cambridge, 1986.
[RR93] H. Raafat and M.A.A. Rashwan. A tree structured neural networks. In ICDAR 93, pages 939-941, 1993.
[SC98] A. Sierra and C. Santa Cruz. Gloabal and local neural network ensembles. PRL, 19:651-655, 1998.
[Sch98] H. Schwenk. The diabolo classifier. Neural Computation, 10:2175-2200, 1998.
[TG03] E. Trentin and M. Gori. Robust combination of neural networks and hidden markov models for speech recognition. IEEE TNN, 14(6):1519-1531, 2003.
[UNKN95] W. Utschick, P. Nachbar, C. Knobloch, and J. A. Nossek. The evaluation of feature extraction criteria applied to neural network classifiers. In ICDAR 95, pages 315-318, 1995.
[Vap82] V. Vapnik. Estimation of Dependencies Based on Empirical Data. Springer-Verlag, New York, NY, 1982.
[Wid90] B. Widrow. 30 years of adaptive neural networks: Perceptron, Madaline, and Backpropagation. IEEE TNN, 78(9):1415-1442, 1990.
[Yam93] K. Yamada. Feedback pattern recognition by inverse recall neural network model. In ICDAR 93, pages 254-257, 1993.
[Yan92] H. Yan. Building a robust nearest neighbor classifier using a multilayer network. International Journal of Neural Systems, 3:361-369, 1992.
[Yan93] H. Yan. Prototype optimization of a nearest neighbor classifier using a multilayer network. Pattern Recognition, 26:317-324, 1993.


This file has been generated by bibtex2html 1.52

Go to main ANNxDAR Bibliography page.

Go to Simone Marinai Home page.