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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_13) on Mon Jul 14 01:37:01 CEST 2008 --><TITLE>Uses of Interface com.rapidminer.operator.Saveable (RapidMiner Class Documentation)</TITLE><LINK REL ="stylesheet" TYPE="text/css" HREF="../../../../stylesheet.css" TITLE="Style"><SCRIPT type="text/javascript">function windowTitle(){ parent.document.title="Uses of Interface com.rapidminer.operator.Saveable (RapidMiner Class Documentation)";}</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" 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Interface<br>com.rapidminer.operator.Saveable</B></H2></CENTER><TABLE BORDER="1" WIDTH="100%" CELLPADDING="3" CELLSPACING="0" SUMMARY=""><TR BGCOLOR="#CCCCFF" CLASS="TableHeadingColor"><TH ALIGN="left" COLSPAN="2"><FONT SIZE="+2">Packages that use <A HREF="../../../../com/rapidminer/operator/Saveable.html" title="interface in com.rapidminer.operator">Saveable</A></FONT></TH></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.example"><B>com.rapidminer.example</B></A></TD><TD>The data core classes of RapidMiner. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.example.set"><B>com.rapidminer.example.set</B></A></TD><TD>The available views (example sets) on the example tables. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator"><B>com.rapidminer.operator</B></A></TD><TD>Provides operators for machine learning and data pre-processing. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.features.transformation"><B>com.rapidminer.operator.features.transformation</B></A></TD><TD>Provides operators for feature space transformations like PCA or ICA. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner"><B>com.rapidminer.operator.learner</B></A></TD><TD>Provides learning operators. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.associations"><B>com.rapidminer.operator.learner.associations</B></A></TD><TD>This package contains classes and operators for association rule mining and frequent item set mining. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.bayes"><B>com.rapidminer.operator.learner.bayes</B></A></TD><TD>This package contains classes and operators for Naive Bayes learning. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.clustering"><B>com.rapidminer.operator.learner.clustering</B></A></TD><TD>Core data structures for the clustering plugin. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.clustering.clusterer"><B>com.rapidminer.operator.learner.clustering.clusterer</B></A></TD><TD>Clustering algorithms. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.clustering.constrained.constraints"><B>com.rapidminer.operator.learner.clustering.constrained.constraints</B></A></TD><TD>Provides helper classes for handling the constraints in constrained and semisupervised clustering schemes. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.clustering.hierarchical.upgma"><B>com.rapidminer.operator.learner.clustering.hierarchical.upgma</B></A></TD><TD>UPGMA Clustering. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.functions"><B>com.rapidminer.operator.learner.functions</B></A></TD><TD>This package contains learners based on the conceptof function approximation. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.functions.kernel"><B>com.rapidminer.operator.learner.functions.kernel</B></A></TD><TD>Learning schemes which make use of kernel functions to transform the feature space, e.g. support vector machines. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.functions.kernel.evosvm"><B>com.rapidminer.operator.learner.functions.kernel.evosvm</B></A></TD><TD>Implementations of SVMs which makes use of general purpose optimizationmethods, e.g. evolutionary strategies or particle swarm optimization. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.functions.kernel.hyperhyper"><B>com.rapidminer.operator.learner.functions.kernel.hyperhyper</B></A></TD><TD>This package contains classes for the HyperHyper learner. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.functions.neuralnet"><B>com.rapidminer.operator.learner.functions.neuralnet</B></A></TD><TD>This package contains a neural net learner based on Joone. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.igss"><B>com.rapidminer.operator.learner.igss</B></A></TD><TD>Provides classes for learning operator Iterating Generic Sequential Sampling. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.igss.hypothesis"><B>com.rapidminer.operator.learner.igss.hypothesis</B></A></TD><TD>Provides the hypothesis classes for learning operator Iterating Generic Sequential Sampling. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.lazy"><B>com.rapidminer.operator.learner.lazy</B></A></TD><TD>Learning schemes which perform lazy learning. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.meta"><B>com.rapidminer.operator.learner.meta</B></A></TD><TD>Meta learning schemes which uses other learning operators to increase the performance. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.rules"><B>com.rapidminer.operator.learner.rules</B></A></TD><TD>Provides rule learners. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.tree"><B>com.rapidminer.operator.learner.tree</B></A></TD><TD>Provides decision tree learners. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.learner.weka"><B>com.rapidminer.operator.learner.weka</B></A></TD><TD>Operators which encapsulate the learning schemes provided by Weka. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.meta"><B>com.rapidminer.operator.meta</B></A></TD><TD>Provides operators for experiment iteration, meta operators, and optimization. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.olap"><B>com.rapidminer.operator.olap</B></A></TD><TD>This package contains some simple operators for basic OLAP analysis like grouping and aggregation. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.performance"><B>com.rapidminer.operator.performance</B></A></TD><TD>Provides performance evaluating operators and performance criteria. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.performance.cost"><B>com.rapidminer.operator.performance.cost</B></A></TD><TD>This package contains cost-based performance evaluations. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.postprocessing"><B>com.rapidminer.operator.postprocessing</B></A></TD><TD>Operators for post processing, usually used for models. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.preprocessing"><B>com.rapidminer.operator.preprocessing</B></A></TD><TD>Operators for preprocessing purposes. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.preprocessing.discretization"><B>com.rapidminer.operator.preprocessing.discretization</B></A></TD><TD>Contains discretization operators which can be used to transform numerical into nominal attributes. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.preprocessing.filter"><B>com.rapidminer.operator.preprocessing.filter</B></A></TD><TD>Containing filter operators changing the input example set, e.g. by removing certain attributes or changing the data. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.preprocessing.normalization"><B>com.rapidminer.operator.preprocessing.normalization</B></A></TD><TD>Preprocessing operators used for normalization. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.similarity"><B>com.rapidminer.operator.similarity</B></A></TD><TD>Basic framework for similarities. </TD></TR><TR BGCOLOR="white" CLASS="TableRowColor"><TD><A HREF="#com.rapidminer.operator.similarity.attributebased"><B>com.rapidminer.operator.similarity.attributebased</B></A></TD><TD>Similarity meausres that are based on the attribute/value representation of objects. </TD></TR>⌨️ 快捷键说明
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