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--- rvkde:usage [2008/08/19 04:10] – dirty
+++ rvkde:usage [2008/08/19 04:21] (current) – dirty
@@ Line 70: / Line 70: @@
 Another common procedure for parameter selection is to create an independent validation set.  For example, you can use __satimage.scale.tr__ and __satimage.scale.val__ to do parameter selection and see how good the parameters are when applying on __satimage.scale.t__.
   * Use __satimage.scale.tr__ (as training set) and __satimage.scale.val__ (as validation set) to select the parameters.<code>
-rvkde-0.2.3-final/rvkde --predict --classify --acc -v rvkde-0.2.3-final/satimage.scale.tr -V rvkde-0.2.3-final/satimage.scale.val -a 1,5,1 -b 1,2,0.5 --ks 1,30,1 --kt 1,30,1
+rvkde-0.2.3-final/rvkde --predict --classify --acc -v rvkde-0.2.3-final/satimage.scale.tr -V rvkde-0.2.3-final/satimage.scale.val -a 1 -b 1,2,0.5 --ks 1,30,1 --kt 1,30,1
 </code>
   * The result looks like<code>
-[0.381232] a=1 b=1.5 s=17 t=10...
+[0.913599] a=1 b=1 s=8 t=23...
 </code>
-  * Predict set2 with the selected model.<code>
+  * Predict __satimage.scale.t__ with the selected parameters.<code>
-rvkde/rvkde --predict --classify --f-measure -v res/tr.x5 -V res/te.x5 -a 1 -b 1.5 --ks 17 --kt 10
+rvkde-0.2.3-final/rvkde --predict --classify --acc -v rvkde-0.2.3-final/satimage.scale.tr -V rvkde-0.2.3-final/satimage.scale.t -a 1 -b 1 --ks 8 --kt 23
 </code>
   * The result looks like<code>
-[0.365651] a=1 b=1.5 s=17 t=10...
+[0.917] a=1 b=1 s=8 t=23...
-</code>It seems that the model selected is better than the previous one.
+</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//.

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