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Top Document: comp.ai.neural-nets FAQ, Part 7 of 7: Hardware Previous Document: How to get invariant recognition of images under Next Document: What about pulsed or spiking NNs? See reader questions & answers on this topic! - Help others by sharing your knowledge URLS: o Don Tveter's The Pattern Recognition Basis of AI at http://www.dontveter.com/basisofai/char.html o Andras Kornai's homepage at http://www.cs.rice.edu/~andras/ o Yann LeCun's homepage at http://www.research.att.com/~yann/ Data sets of handwritten digits can be found at http://www.research.att.com/~yann/exdb/mnist/ Other references: Hastie, T., and Simard, P.Y. (1998), "Metrics and models for handwritten character recognition," Statistical Science, 13, 54-65. Jackel, L.D. et al., (1994) "Comparison of Classifier Methods: A Case Study in Handwritten Digit Recognition", 1994 International Conference on Pattern Recognition, Jerusalem LeCun, Y., Jackel, L.D., Bottou, L., Brunot, A., Cortes, C., Denker, J.S., Drucker, H., Guyon, I., Muller, U.A., Sackinger, E., Simard, P., and Vapnik, V. (1995), "Comparison of learning algorithms for handwritten digit recognition," in F. Fogelman and P. Gallinari, eds., International Conference on Artificial Neural Networks, pages 53-60, Paris. Orr, G.B., and Mueller, K.-R., eds. (1998), Neural Networks: Tricks of the Trade, Berlin: Springer, ISBN 3-540-65311-2. User Contributions:Comment about this article, ask questions, or add new information about this topic:Top Document: comp.ai.neural-nets FAQ, Part 7 of 7: Hardware Previous Document: How to get invariant recognition of images under Next Document: What about pulsed or spiking NNs? Part1 - Part2 - Part3 - Part4 - Part5 - Part6 - Part7 - Single Page [ Usenet FAQs | Web FAQs | Documents | RFC Index ] Send corrections/additions to the FAQ Maintainer: saswss@unx.sas.com (Warren Sarle)
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comp.ai.neural-nets FAQ, Part 7 of 7: Hardware
Section - How to recognize handwritten characters?
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comp.ai.neural-nets FAQ, Part 7 of 7: Hardware
Section - How to recognize handwritten characters?
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PDP++ is a neural-network simulation system written in C++, developed as an advanced version of the original PDP software from McClelland and Rumelhart's "Explorations in Parallel Distributed Processing Handbook" (1987). The software is designed for both novice users and researchers, providing flexibility and power in cognitive neuroscience studies. Featured in Randall C. O'Reilly and Yuko Munakata's "Computational Explorations in Cognitive Neuroscience" (2000), PDP++ supports a wide range of algorithms. These include feedforward and recurrent error backpropagation, with continuous and real-time models such as Almeida-Pineda. It also incorporates constraint satisfaction algorithms like Boltzmann Machines, Hopfield networks, and mean-field networks, as well as self-organizing learning algorithms, including Self-organizing Maps (SOM) and Hebbian learning. Additionally, it supports mixtures-of-experts models and the Leabra algorithm, which combines error-driven and Hebbian learning with k-Winners-Take-All inhibitory competition. PDP++ is a comprehensive tool for exploring neural network models in cognitive neuroscience.