Publisher - Neural Decision Lab LLC

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Software publisher info

Publisher:
Neural Decision Lab LLC
Webpage:
www.NeuralDL.com
Rating:
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Publisher Description
Given numerical training data, nonlinear networks (including neural nets) learn how to map N-dimensional input vectors into M-dimensional output vectors. This makes nonlinear networks an ideal solution to a number of problems including pattern recognition, function approximation, data mining, and e-mail spam filtering. The diagram illustrates how our optimization system can use nonlinear networks to boost your application!
Latest Windows software from Neural Decision Lab LLC


MLP-ClassCategory:  Business & Finance » Other
Source code for fast training of multilayer perceptron classifier. Example training and validation data files included. Source code for applying the trained network. Resulting networks can be pruned by NuClass7.06. The training algorithm can be modified to be substantially better than BP. For winzip password, go to :http://www-ee.uta.edu/eeweb/ip/Software/Software.htm

MLP-MapCategory:  Business & Finance » Other
Source code for fast training of multilayer perceptron for approximation. Example training and validation data files included. Source code for applying the trained network. Resulting networks can be pruned by NuMap7.06. The training algorithm can be modified to be substantially better than BP. For winzip password, go to :http://www-ee.uta.edu/eeweb/ip/Software/Software.htm

Nuclass7Category:  Business & Finance » Other
Freeware for fast training, validation, and application of neural and conventional classifiers including multilayer perceptron, functional link network, piecewise linear network, self organizing map and K-Means. C source provided for applying trained networks. Extensive help. User-supplied txt-format training data files, containing rows of numbers, can be of any size. Pruning for approximate structural risk minimization.

Numap7Category:  Business & Finance » Other
Freeware for fast training, validation, and application of regression/approximation networks including the multilayer perceptron, functional link network, piecewise linear network, self organizing map and K-Means. C source for applying trained networks. Extensive help. User-supplied txt-format training data files, containing rows of numbers, can be of any size. Pruning for approximate structural risk minimization.