KnowledgeMiner (yX) for Excel

A knowledge mining tool that is using data stored in Microsoft Excel
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KnowledgeMiner (yX) for Excel Ranking & Summary

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  • Rating:
  • License:
  • Demo
  • Price:
  • USD 800.00 | BUY the full version
  • Publisher Name:
  • Script Software
  • Publisher web site:
  • http://www.scriptsoftware.com/
  • Operating Systems:
  • Mac OS X 10.5 or later
  • File Size:
  • 21.5 MB

KnowledgeMiner (yX) for Excel Tags


KnowledgeMiner (yX) for Excel Description

A knowledge mining tool that is using data stored in Microsoft Excel KnowledgeMiner (yX) for Excel is a knowledge mining tool that use data stored in Microsoft Excel for building predictive and descriptive models from this data autonomously and easily. KnowledgeMiner (yX) for Excel supports both major releases of Microsoft Excel, 2004 and 2008. The modeling engine of KnowledgeMiner (yX) for Excel implements unique modeling technologies which are built on the principles of self-organization: Learning from noisy data an unknown relationship between output and input of any given system in an evolutionary way from a very simple model to an optimal complex one which generalizes well.KnowledgeMiner (yX) for Excel implements a completely redesigned and redeveloped modeling engine called (yX). KnowledgeMiner (yX) for Excel is based on the modeling technologies also found in our established KnowledgeMiner 6.0 software, which has been successfully used by our customers for more than 10 years.It is 64-bit parallel software. It allows to take full advantage of multi-processor and/or multi-core based Macs. KnowledgeMiner (yX) for Excel is able to scale to the number of processing elements detected at runtime. Requirements: · Microsoft Excel Limitations: · Maximum number of network layers: 5. · Maximum forecast horizon: 5. · Exporting models to Excel: Auto-regressive models only. · Writing predictions to an Excel sheet disabled. · Maximum number of samples: 1000. · Maximum pattern length: 10. · Maximum forecast horizon: 5. What's New in This Release: · High-dimensional modeling which scales to data sets of very small (< 20) to large number of samples (


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