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 </code>It reveals that RVKDE yields very close accuracies with these two parameter selection schemes.
-====== Summary of RVKDE ======
-Until now, you know that RVKDE has four parameters (//alpha//, //beta//, //ks//, and //kt//).  rvkde is a sophisticated machine learning package which has built-in functionalities for cross-validation and parameter-enumeration.  We will see some other parameters of rvkde in future exercises.  Of course, you could check [[http://zoro.ee.ncku.edu.tw/mbincku/rvkde/|the homepage of rvkde]] if you want to learn these facilities now.
-In addition, you learn two common model selection procedures.  In this exercise, you must try to find the best model for prediction the whole dataset, that is, F-measures of both //te.x5// and //te.x10// are good using only //tr.x5//, //va.x5//, //tr.x10//, and //va.x10//.