I am trying to use an SVM for the first time. Here is my setup:
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.1.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.1.001" expanded="true" name="Process">
<process expanded="true" height="615" width="964">
<operator activated="true" class="retrieve" compatibility="5.1.001" expanded="true" height="60" name="Retrieve" width="90" x="112" y="165">
<parameter key="repository_entry" value="//MLData/FirstData"/>
</operator>
<operator activated="true" class="support_vector_machine_libsvm" compatibility="5.1.001" expanded="true" height="76" name="SVM" width="90" x="246" y="165">
<list key="class_weights"/>
</operator>
<operator activated="true" class="apply_model" compatibility="5.1.001" expanded="true" height="76" name="Apply Model" width="90" x="380" y="165">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance" compatibility="5.1.001" expanded="true" height="76" name="Performance" width="90" x="514" y="165"/>
<connect from_op="Retrieve" from_port="output" to_op="SVM" to_port="training set"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="result 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
</process>
</operator>
</process>
I am just trying to apply my model to the training data in hopes of seeing very high prediction...after that I plan to examine more complicated / useful cases. But my predictions are very poor. How do I improve the performance of SVM learning? I have heard / can learn parameter optimization, but isn't there some strictness setting that will at least result in 100% accuracy given though support vectors?
