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Top Document: comp.ai.neural-nets FAQ, Part 4 of 7: Books, data, etc. Previous Document: Mailing lists, BBS, CD-ROM? Next Document: Databases for experimentation with NNs? See reader questions & answers on this topic! - Help others by sharing your knowledge The NN benchmarking resources page at http://wwwipd.ira.uka.de/~prechelt/NIPS_bench.html was created after a NIPS 1995 workshop on NN benchmarking. The page contains pointers to various papers on proper benchmarking methodology and to various sources of datasets. Benchmark studies require some familiarity with the statistical design and analysis of experiments. There are many textbooks on this subject, of which Cohen (1995) will probably be of particular interest to researchers in neural nets and machine learning (see also the review of Cohen's book by Ron Kohavi in the International Journal of Neural Systems, which can be found on-line at http://robotics.stanford.edu/users/ronnyk/ronnyk-bib.html). Reference: Cohen, P.R. (1995), Empirical Methods for Artificial Intelligence, Cambridge, MA: The MIT Press. User Contributions:Comment about this article, ask questions, or add new information about this topic:Top Document: comp.ai.neural-nets FAQ, Part 4 of 7: Books, data, etc. Previous Document: Mailing lists, BBS, CD-ROM? Next Document: Databases for experimentation with 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)
Last Update March 27 2014 @ 02:11 PM
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comp.ai.neural-nets FAQ, Part 4 of 7: Books, data, etc.
Section - How to benchmark learning methods?
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comp.ai.neural-nets FAQ, Part 4 of 7: Books, data, etc.
Section - How to benchmark learning methods?
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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.