title: Why MaxEntropy in weka always results in JVM out of heap?
body: <p>i am trying Maximum Entropy in weka for text classification. I am using Logistic Regression in Weka which is equivalent to Max Entropy. I read that its is computantially expensive. I have current setting of 2G alloted to JVM and i keep word vector dimension to 10, 000 to evaluate Max Entropy, However it always results in JVM out of memory. This makes me think i am making any mistake because 2G heap size is too enough for any classifier, isn't it ?</p>
  
  <p>1) Have anyone used MaxEnt(Logistic.Java) in Weka ? Is it supposed to be so slow for text classification ? </p>
  
  <p>2) Is there any parameter tunning necessary for MaxEnt which i may be ignoring ?</p>