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NeuroXL Package 1.7
Program Specifications
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| Version: |
1.7
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| Size: |
1.30 MB |
| Publisher: |
OLSOFT |
| Date Added: |
Mar 9, 2006 |
| License [?]: |
Shareware (USD$99.95 to buy) |
| Operating System: |
Windows XP, Windows NT, Windows ME, Windows 98, Windows 2000 |
| Requirements: |
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| Download Links: |
Download NeuroXL Package
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BumperSoft Editor's Review Status:
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Publisher's Description of NeuroXL Package
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" Neural network software that works in the Microsoft Excel environment. "
- From OLSOFT
The NeuroXL Package provides you with unbeatable value by combining both our neural network add-in tools for Microsoft Excel in one low price package. This powerful combination of software will provide you with the ability to quickly and accurately solve a full range of real-world forecasting, pattern recognition, and data mining problems.
NeuroXL Classifier is a fast, powerful and easy-to-use neural network software tool for classifying data in Microsoft Excel. Designed to aid experts in real-world data mining and pattern recognition tasks, it hides the underlying complexity of neural network processes while providing graphs and statistics for the user to easily understand results. NeuroXL Classifier uses only proven algorithms and techniques, and integrates seamlessly with Microsoft Excel.
NeuroXL Predictor is a neural network forecasting tool that quickly and accurately solves forecasting and estimation problems in Microsoft Excel. It is designed from the ground-up to aid experts in solving real-world forecasting problems. The NeuroXL Predictor interface is easy-to-use and intuitive, does not require any prior knowledge of neural networks, and is integrated seamlessly with Microsoft Excel. The software brings increased precision and accuracy to a wide variety of tasks, including stock price prediction, sales forecasting, and sports score prediction.
Both applications use only proven algorithms and techniques, integrate seamlessly with Microsoft Excel, and provide easy-to-use and intuitive interfaces. They also both hide the underlying complexity of neural network processes while providing graphs and statistics for the user to easily understand results.
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