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Directory Classifier lets you view a customizable listing of the contents for any directory on your computer. You can easily save the folder listing as TXT or PDF file. The Directory Classifier listing can be saved as a file that can be imported into most popular spreadsheets and database applications. Directory Classifier can also process information contained within a folder's subfolders. A wide range of options are available to allow you customize your listing, including font style and size. Directory Classifier also allows you to specify what detailed information to list, as well as to apply filters to limit the files that are included in the output list. There are also a lot of settings for the generated PDF files, including PDF Security (passwords, permissions). Page orientation, fonts and colors can also be modified.
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NUCEM? improves your personal productivity by eliminating what you perceive to be spam from your Inbox. No other solution can do that for you. NUCEM? installs in minutes and is completely intuitive so you are spam free without complicated configurations or lengthy user manuals. NUCEM? blocks over 99% of spam from the Inbox, and continues to improve as you train it to recognize e-mail that you perceive to be spam and you can train NUCEM? to trust newsletters that look like spam. NUCEM? is 3 times more effective at blocking spam than compared with the next best anti-spam service, and almost 10 times more effective compared with the next best anti-spam product.
NUCEM? is seamlessly integrated into Microsoft Outlook, so you continue to work in your familiar e-mail program. Using a proprietary e-mail classifier NUCEM? accurately differentiates between valid e-mail and spam because spam uses distinct patterns, styles and vocabulary, which is distinguishable from traditional e-mail. This technology is extremely accurate (99%) in detecting spam, while exhibiting a very lower incidence of detecting valid e-mail as spam (false-positive).
The key distinguishable characteristics of NUCEM? are:
* Accurate Spam Detection > 99%
* False Positive Detection < 0.1%
* Limit size of quarantine folder
* Simple One Click Solution
* Immune to spammer's avoidance techniques
* Supports Hotmail, IMAP, Yahoo, POP and Exchange Accounts
* Customized Spam Definition
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LTF-Cimulator is a software for easy simulation and development of LTF-C neural networks. You can train and test a network, save it in a readable text format or perform cross-validation. Training parameters can be chosen automatically by LTF-Cimulator. Exemplary datasets are included, which will help you become more familiar with the program.
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Freeware for fast development and application of classification type networks including the multilayer perceptron (MLP), functional link network, piecewise linear network, nearest neighbor classifier (NNC), self organizing map (SOM) and K-Means clustering. 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 classification error and SOM cluster formation are included. Extensive help files are provided in the software.
Although principally aimed at scientists and engineers, Nuclass7 is highly automated and requires very few parameter choices by the user. Advanced features include a fast MLP training algorithm (faster than BP and better than LM), input feature selection, pruning (elimination) of useless units (for MLP) and modules for PLN). Utilities are provided for counting patterns, deleting columns, combining files, splitting files, calculating column mean and standard deviation, and plotting column histograms. Training data can be compressed using the discrete Karhunen-Loeve' transform (KLT). This freeware version of Nuclass7 limits the MLP to 10 hidden units, the PLN to 10 clusters, and the NNC to 50 clusters. The commercial version, which lacks these limitations, is also available for those who need it. The regression/approximation version of this software, called Numap7, is also available. Nuclass7 was developed by the Image Processing and Neural Networks Lab of Univ. of Texas at Arlington.
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This installation program includes evaluation versions of three products for neural network design and development: NeuroSolutions, NeuroSolutions for Excel and the Custom Solution Wizard. NeuroSolutions is a highly graphical neural network development tool for Windows 95/98/Me/NT/2000. This leading edge software combines a modular, icon-based network design interface with an implementation of advanced learning procedures, such as recurrent backpropagation, backpropagation through time and genetic optimization. The result is a virtually unconstrained environment for designing neural networks for research or to solve real-world problems. NeuroSolutions for Excel is a Microsoft Excel add-in that can be used in conjunction with NeuroSolutions to simplify and enhance the process of getting data in and out of the network. The Custom Solution Wizard is a program that will take any neural network created with NeuroSolutions and automatically generate and compile a Dynamic Link Library (DLL) for that network, which you can then embed into your own application.
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The NeuroSolutions for MATLAB neural network toolbox is a valuable addition to MATLAB's technical computing capabilities allowing users to leverage the power of NeuroSolutions (www.neurosolutions.com) inside MATLAB and Simulink. The toolbox features 15 neural models, 5 learning algorithms and a host of useful utilities integrated in an easy-to-use interface, which requires ?next to no knowledge? of neural networks to begin using the product. The toolbox is also integrated with NeuroSolutions, which enables users to build custom networks in NeuroSolutions and use it inside Matlab using the NeuroSolutions for Matlab interface. The free evaluation edition of NeuroSolutions for MATLAB includes several demos that will walk you step-by-step through the product. In addition, you can experiment with all of the features of this product using your own data. See how easy it is to solve your problems using neural networks inside the MATLAB environment.
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