"SVM Runtime"

Ghostrider

How long would it take an SVM to train on a 12 input, 1000 or 2000 row dataset?  How about a Neural Network?  Would the neural network train faster or slower than SVM?  Assume that it would be running on a modern quad-core i7.  I don't have data yet, just an idea...wondering if it's feasible.  By the way, are there any plans for RapidMiner to support OpenCL or GPU computing?

haddock

No need to wait for the data - you can generate it and test for yourself, like this..

<?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="273" width="872">
      <operator activated="true" class="generate_data" compatibility="5.1.001" expanded="true" height="60" name="Generate Data" width="90" x="6" y="75">
        <parameter key="target_function" value="complicated function"/>
        <parameter key="number_examples" value="20000"/>
        <parameter key="number_of_attributes" value="12"/>
      </operator>
      <operator activated="true" class="support_vector_machine_libsvm" compatibility="5.1.001" expanded="true" height="76" name="SVM" width="90" x="265" y="72">
        <parameter key="svm_type" value="epsilon-SVR"/>
        <list key="class_weights"/>
      </operator>
      <connect from_op="Generate Data" from_port="output" to_op="SVM" to_port="training set"/>
      <connect from_op="SVM" from_port="model" 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>

Over to Ingo on the GPU front ...