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<HTML>  <HEAD>    <!--SCRIPT LANGUAGE="JavaScript" SRC="http://a1835.g.akamai.net/f/1835/276/3h/www.netlibrary.com/include/js/dictionary_library.js"></SCRIPT>    <SCRIPT LANGUAGE="JavaScript">      if (!opener){document.onkeyup=parent.turnBookPage;}    </SCRIPT!-->    <META HTTP-EQUIV="Cache-Control" CONTENT="no-cache">    <META HTTP-EQUIV="Pragma" CONTENT="no-cache">    <META HTTP-EQUIV="Expires" CONTENT="-1"><META http-equiv="Content-Type" content="text/html; charset=windows-1252"><SCRIPT>var PrevPage="Page_167";var NextPage="Page_169";var CurPage="Page_168";var PageOrder="177";</SCRIPT>  <TITLE>Document</TITLE>  </HEAD>  <BODY BGCOLOR="#FFFFFF"><CENTER><TABLE BORDER=0 WIDTH=100% CELLPADDING=0><TR><TD ALIGN=CENTER>  <TABLE BORDER=0 CELLPADDING=2 CELLSPACING=0 WIDTH=100%>  <TR>  <TD ALIGN=LEFT><A HREF='Page_167.html'>Previous</A></TD>  <TD ALIGN=RIGHT><A HREF='Page_169.html'>Next</A></TD>  </TR>  </TABLE></TD></TR><TR><TD ALIGN=LEFT><P><A NAME='JUMPDEST_Page_168'/><A NAME='{5DD}'/><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0 WIDTH='100%'><TR><TD ALIGN=RIGHT><FONT FACE='Times New Roman, Times, Serif' SIZE=2 COLOR=#FF0000>Page 168</FONT></TD></TR></TABLE><A NAME='{5DE}'/><TABLE CELLPADDING=0 CELLSPACING=0 BORDER=0 WIDTH='100%'><TR><TD HEIGHT=12></TD></TR><TR><TD><TABLE CELLSPACING=0 BGCOLOR=#C0C0C0 BORDER=1 BORDERCOLOR=#000000 WIDTH=616 CELLPADDING=5><TR><TD ROWSPAN=2 WIDTH=616 VALIGN=TOP><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0 WIDTH='100%'><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD ALIGN=CENTER><FONT FACE='Times New Roman, Times, Serif' SIZE=2><B>NGO NeuroGenetic Optimizer</B></FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=2><B>Best At:</B> Classification and Prediction</FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=2><B>Cost Factor:</B> No</FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=2><B>Current Version:</B> 2.1</FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=2><B>Data Access:</B> ASCII delimited</FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=2><B>Data Preprocessing:</B> Yes, with EMS</FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=2><B>Distinguishing Feature:</B> Accuracy</FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=2><B>File Limitations:</B> None</FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=2><B>Installed Base:</B> 350</FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=2><B>Memory Required:</B> 16 MB</FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=2><B>Parallel Processing Support:</B> Yes</FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=2><B>Pricing:</B> $295 and up</FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=2><B>Solution Format:</B> API</FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=2><B>Space Required :</B> 5 MB</FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=2><B>Training/Consulting:</B> Yes</FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=2><B>Technology Paradigm:</B> Genetic Algorithm and Multiple Neural Networks</FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE></TD></TR></TABLE></TD></TR></TABLE><BR><A NAME='{5DF}'/><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=3><B><I>NGO NeuroGenetic Optimizer</I></B></FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><A NAME='{5E0}'/><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=3>The Neuro Genetic Optimizer (NGO) is a self-optimizing neural network model-generating tool. It uses a genetic algorithm component to select key data variables and optimize the structure of a back propagation, a Self-Organizing Map, and other neural network architectures to maximize the predictive accuracy of its models.</FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><A NAME='{5E1}'/><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=3>The concept behind this tool is that rather than building one model at a time, you build multiple ones simultaneously. This particular approach to data mining and model construction lends itself quite well to dynamic business applications requiring multiple forecasting models. For example, BioComp's Enterprise Modeling Server can generate multiple models for each product an e-commerce site may carry. Another feature this tools has going for it is that of combating model deterioration. As with any other types of models, neural network models tend to decay with time. Having the ability to quickly refresh them with new data is clearly an asset, which this tool delivers.</FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><A NAME='{5E2}'/><TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0><TR>  <TD ROWSPAN=5></TD>  <TD COLSPAN=3 HEIGHT=12></TD>  <TD ROWSPAN=5></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR><TD></TD>  <TD><FONT FACE='Times New Roman, Times, Serif' SIZE=3>The NGO uses genetic algorithms to evolve neural network structures while simultaneously searching for significant input variables to maximize the predictive accuracy of its neural network models. The effectiveness of the genetic algorithms' search capabilities in optimizing the settings of a neural network is an excellent idea. for example, a problem consisting of finding the best combination (subset) of 20 inputs and up to 15 hidden nodes in a back-propagation neural network is a combinatiorial problem with over 16 million permutations. According to BioComp Systems, an excellent solution often appears in less than 1500 evaluations, which is 0.009 percent of the total possible configurations of a neural network. Commonly highly fit networks are often found in the first 30 to 50 neural networks evaluated. This is clearly an efficient.</FONT><FONT FACE='Times New Roman, Times, Serif' SIZE=3 COLOR=#FFFF00><!-- continue --></FONT></TD><TD></TD></TR><TR>  <TD COLSPAN=3></TD></TR><TR>  <TD COLSPAN=3 HEIGHT=1></TD></TR></TABLE><A NAME='{5E3}'/></FORM></P></TD></TR></TABLE><P><FONT SIZE=0 COLOR=WHITE></CENTER><A NAME="bottom">&nbsp;</A><!-- netLibrary.com Copyright Notice -->  </BODY></HTML>

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