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Numap7
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| Publisher: |
Neural Decision Lab LLC
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Contact Email: |
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Free to use |
| Version: |
7.06a |
File Size: |
13.79MB |
| Date Released: |
2006-01-11 |
Release Status: |
Major Update |
| License: |
Freeware |
Price: |
Free |
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(13)
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| Date Added: |
2006-06-17 |
Date Updated: |
2006-06-30 |
| Category: |
Business::Math & Scientific Tools |
| Platform(s): |
Windows2000,WinXP |
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| Rating: |
 (Votes: 1)
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 Freeware for fast training, validation, and application of regression/approximation networks including the multilayer perceptron (MLP), functional link network, and piecewise linear network. The self organizing map (SOM) and K-Means clustering are also included. Fast pruning algorithms create and validate a nested sequence of different size networks, to facilitate structural risk minimization. C source code for applying trained networks is provided, so users can use networks in their own applications. User-supplied txt-format training data files, containing rows of numbers, can be of any size. Example training data is also provided. Fast VB Graphics for network training error and cluster formation are included. Extensive help files are provided in the software.
Numap7 is highly automated and requires very few parameter choices by the user. This version runs significantly faster. Advanced features include network sizing and feature selection. Training data can be compressed using the discrete Karhunen-Loeve' transform (KLT). This basic version of Numap7 limits the MLP to 10 hidden units and limits the PLN to 10 clusters. Upgradable to commercial versions which lack these limitations. The classification (decision making) version of this software, called Nuclass7, is also available. Numap7.0 was developed by the Image Processing and Neural Networks Lab of Univ. of Texas at Arlington, and by Neural Decision Lab LLC.
Keywords: neural network, multilayer perceptron, fast training, validation, regression, approximation
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| Other softwares by this publisher
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Nuclass7
Freeware for fast training, validation, and application of neural and conventional classifiers including MLP, functional link net, piecewise linear net, nearest neighbor classifier, self organizing map. Extensive help. C source for trained nets
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