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--- rvkde:usage [2008/08/19 04:16] – dirty
+++ rvkde:usage [2008/08/19 04:21] (current) – dirty
@@ Line 79: / Line 79: @@
 </code>
   * The result looks like<code>
-[0.9075] a=1 b=1 s=8 t=23...
+[0.917] a=1 b=1 s=8 t=23...
-</code>Despite the different __ks__ values, RVKDE yields the same accuracy of 0.9075  two selection schemes results in different __ks__ values, RVKDE yields the same accuracy of 0.9175 under this parameter combination when using satimage.scale to predict satimage.scale.t.
+</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//.