H1.1 Mathematical theories of neural networks

A recirculating document handling apparatus and method for plurally recirculating original document sheets to and from a stack thereof and to and from the imaging station of a copier for providing precollation copying, providing selected ones of different but compact and partially shared document recirculation loop paths within a common loop path, selected depending on the copying mode selected with path selector means.

[1]  S. Amari,et al.  A Mathematical Foundation for Statistical Neurodynamics , 1977 .

[2]  Klaus-Robert Müller,et al.  Asymptotic statistical theory of overtraining and cross-validation , 1997, IEEE Trans. Neural Networks.

[3]  Shun-ichi Amari,et al.  Information geometry of Boltzmann machines , 1992, IEEE Trans. Neural Networks.

[4]  Shun-ichi Amari,et al.  Network information criterion-determining the number of hidden units for an artificial neural network model , 1994, IEEE Trans. Neural Networks.

[5]  Robert A. Jacobs,et al.  Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.

[6]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[8]  Kaoru Nakano,et al.  Associatron-A Model of Associative Memory , 1972, IEEE Trans. Syst. Man Cybern..

[9]  J. Cowan,et al.  Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.

[10]  E M Harth,et al.  Brain functions and neural dynamics. , 1970, Journal of theoretical biology.

[11]  Shun-ichi Amari,et al.  Statistical Theory of Learning Curves under Entropic Loss Criterion , 1993, Neural Computation.

[12]  Sherrington,et al.  Dynamics of fully connected attractor neural networks near saturation. , 1993, Physical review letters.

[13]  Heskes,et al.  Learning processes in neural networks. , 1991, Physical review. A, Atomic, molecular, and optical physics.

[14]  Masato Okada,et al.  Notions of Associative Memory and Sparse Coding , 1996, Neural Networks.

[15]  L. Jones A Simple Lemma on Greedy Approximation in Hilbert Space and Convergence Rates for Projection Pursuit Regression and Neural Network Training , 1992 .

[16]  T. Watkin,et al.  THE STATISTICAL-MECHANICS OF LEARNING A RULE , 1993 .

[17]  Andrew R. Barron,et al.  Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.

[18]  Haim Sompolinsky,et al.  On-line Learning of Dichotomies: Algorithms and Learning Curves. , 1995, NIPS 1995.

[19]  S. Amari A Theory ofAdaptive Pattern Classifiers , 1967 .

[20]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[21]  C. Malsburg,et al.  How patterned neural connections can be set up by self-organization , 1976, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[22]  S. Amari,et al.  Formation of topographic maps and columnar microstructures in nerve fields , 1979, Biological Cybernetics.

[23]  Shun-ichi Amari,et al.  A Theory of Adaptive Pattern Classifiers , 1967, IEEE Trans. Electron. Comput..