论文信息 - Neural network classification and formalization

Neural network classification and formalization

Abstract In order to assist the field of neural networks in maturing, a formalization and a solid foundation are essential. Additionally, to permit the introduction of formal proofs, it is essential to have an all-encompassing formal mathematical definition of a neural network. This publication offers a neural network formalization consisting of a topological taxonomy, a uniform nomenclature, and an accompanying consistent mnemonic notation. Supported by this formalization, both a flexible hierarchical and a universal mathematical definition are presented.

Emile Fiesler | E. Fiesler

[1] Russell C. Eberhart. Standardization of neural network terminology , 1990, IEEE Trans. Neural Networks.

[2] Philip D. Wasserman,et al. Neural computing - theory and practice , 1989 .

[3] Stephen A. Ritz,et al. Distinctive features, categorical perception, and probability learning: some applications of a neural model , 1977 .

[4] Colin Giles,et al. Learning, invariance, and generalization in high-order neural networks. , 1987, Applied optics.

[5] Masafumi Hagiwara. Multidirectional associative memory , 1990 .

[6] Leslie G. Valiant,et al. Functionality in neural nets , 1988, COLT '88.

[7] S Mangrulkar,et al. Artificial neural systems. , 1990, ISA transactions.

[8] Jia-Wei Hong. On connectionist models , 1988 .

[9] Judith E. Dayhoff,et al. Neural Network Architectures: An Introduction , 1989 .

[10] Yoh-Han Pao,et al. Adaptive pattern recognition and neural networks , 1989 .

[11] David E. Rumelhart,et al. Product Units: A Computationally Powerful and Biologically Plausible Extension to Backpropagation Networks , 1989, Neural Computation.

[12] W. H. Clingman,et al. Algebraic analysis of neural net learning , 1989, International 1989 Joint Conference on Neural Networks.

[13] P. Werbos,et al. Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .