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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd"><!--NewPage--><HTML><HEAD><!-- Generated by javadoc (build 1.5.0_10) on Fri Jan 26 16:36:09 NZDT 2007 --><TITLE>weka.classifiers.mi</TITLE><META NAME="keywords" CONTENT="weka.classifiers.mi package"><LINK REL ="stylesheet" TYPE="text/css" HREF="../../../stylesheet.css" TITLE="Style"><SCRIPT type="text/javascript">function windowTitle(){ parent.document.title="weka.classifiers.mi";}</SCRIPT><NOSCRIPT></NOSCRIPT></HEAD><BODY BGCOLOR="white" onload="windowTitle();"><!-- ========= START OF TOP NAVBAR ======= --><A NAME="navbar_top"><!-- --></A><A HREF="#skip-navbar_top" title="Skip navigation links"></A><TABLE BORDER="0" WIDTH="100%" CELLPADDING="1" CELLSPACING="0" SUMMARY=""><TR><TD COLSPAN=2 BGCOLOR="#EEEEFF" CLASS="NavBarCell1"><A NAME="navbar_top_firstrow"><!-- --></A><TABLE BORDER="0" CELLPADDING="0" CELLSPACING="3" SUMMARY=""> <TR ALIGN="center" VALIGN="top"> <TD BGCOLOR="#EEEEFF" 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home</B></FONT></A> </TD> </TR></TABLE></TD><TD ALIGN="right" VALIGN="top" ROWSPAN=3><EM></EM></TD></TR><TR><TD BGCOLOR="white" CLASS="NavBarCell2"><FONT SIZE="-2"> <A HREF="../../../weka/classifiers/meta/nestedDichotomies/package-summary.html"><B>PREV PACKAGE</B></A> <A HREF="../../../weka/classifiers/mi/supportVector/package-summary.html"><B>NEXT PACKAGE</B></A></FONT></TD><TD BGCOLOR="white" CLASS="NavBarCell2"><FONT SIZE="-2"> <A HREF="../../../index.html?weka/classifiers/mi/package-summary.html" target="_top"><B>FRAMES</B></A> <A HREF="package-summary.html" target="_top"><B>NO FRAMES</B></A> <SCRIPT type="text/javascript"> <!-- if(window==top) { document.writeln('<A HREF="../../../allclasses-noframe.html"><B>All Classes</B></A>'); } //--></SCRIPT><NOSCRIPT> <A HREF="../../../allclasses-noframe.html"><B>All Classes</B></A></NOSCRIPT></FONT></TD></TR></TABLE><A NAME="skip-navbar_top"></A><!-- ========= END OF TOP NAVBAR ========= --><HR><H2>Package weka.classifiers.mi</H2><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TH ALIGN="left" COLSPAN="2"><FONT SIZE="+2"><B>Class Summary</B></FONT></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/mi/CitationKNN.html" title="class in weka.classifiers.mi">CitationKNN</A></B></TD><TD>Modified version of the Citation kNN multi instance classifier.<br/> <br/> For more information see:<br/> <br/> Jun Wang, Zucker, Jean-Daniel: Solving Multiple-Instance Problem: A Lazy Learning Approach.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/mi/MDD.html" title="class in weka.classifiers.mi">MDD</A></B></TD><TD>Modified Diverse Density algorithm, with collective assumption.<br/> <br/> More information about DD:<br/> <br/> Oded Maron (1998).</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/mi/MIBoost.html" title="class in weka.classifiers.mi">MIBoost</A></B></TD><TD>MI AdaBoost method, considers the geometric mean of posterior of instances inside a bag (arithmatic mean of log-posterior) and the expectation for a bag is taken inside the loss function.<br/> <br/> For more information about Adaboost, see:<br/> <br/> Yoav Freund, Robert E.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/mi/MIDD.html" title="class in weka.classifiers.mi">MIDD</A></B></TD><TD>Re-implement the Diverse Density algorithm, changes the testing procedure.<br/> <br/> Oded Maron (1998).</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/mi/MIEMDD.html" title="class in weka.classifiers.mi">MIEMDD</A></B></TD><TD>EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm.<br/> It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/mi/MILR.html" title="class in weka.classifiers.mi">MILR</A></B></TD><TD>Uses either standard or collective multi-instance assumption, but within linear regression.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/mi/MINND.html" title="class in weka.classifiers.mi">MINND</A></B></TD><TD>Multiple-Instance Nearest Neighbour with Distribution learner.<br/> <br/> It uses gradient descent to find the weight for each dimension of each exeamplar from the starting point of 1.0.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/mi/MIOptimalBall.html" title="class in weka.classifiers.mi">MIOptimalBall</A></B></TD><TD>This classifier tries to find a suitable ball in the multiple-instance space, with a certain data point in the instance space as a ball center.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/mi/MISMO.html" title="class in weka.classifiers.mi">MISMO</A></B></TD><TD>Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.<br/> <br/> This implementation globally replaces all missing values and transforms nominal attributes into binary ones.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/mi/MISVM.html" title="class in weka.classifiers.mi">MISVM</A></B></TD><TD>Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL).</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/mi/MIWrapper.html" title="class in weka.classifiers.mi">MIWrapper</A></B></TD><TD>A simple Wrapper method for applying standard propositional learners to multi-instance data.<br/> <br/> For more information see:<br/> <br/> E.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/mi/SimpleMI.html" title="class in weka.classifiers.mi">SimpleMI</A></B></TD><TD>Reduces MI data into mono-instance data.</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/mi/TLD.html" title="class in weka.classifiers.mi">TLD</A></B></TD><TD>Two-Level Distribution approach, changes the starting value of the searching algorithm, supplement the cut-off modification and check missing values.<br/> <br/> For more information see:<br/> <br/> Xin Xu (2003).</TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD WIDTH="15%"><B><A HREF="../../../weka/classifiers/mi/TLDSimple.html" title="class in weka.classifiers.mi">TLDSimple</A></B></TD><TD>A simpler version of TLD, mu random but sigma^2 fixed and estimated via data.<br/> <br/> For more information see:<br/> <br/> Xin Xu (2003).</TD></TR></TABLE> <P><DL></DL><HR><!-- ======= 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