[GRASS-SVN] r68587 - grass-addons/grass7/vector/v.class.mlR

[svn_grass at osgeo.org]

Author: mlennert
Date: 2016-06-03 01:59:18 -0700 (Fri, 03 Jun 2016)
New Revision: 68587

Modified:
   grass-addons/grass7/vector/v.class.mlR/v.class.mlR.html
   grass-addons/grass7/vector/v.class.mlR/v.class.mlR.py
Log:
v.class.mlR: added literature reference and improved doc


Modified: grass-addons/grass7/vector/v.class.mlR/v.class.mlR.html
===================================================================
--- grass-addons/grass7/vector/v.class.mlR/v.class.mlR.html	2016-06-03 02:46:29 UTC (rev 68586)
+++ grass-addons/grass7/vector/v.class.mlR/v.class.mlR.html	2016-06-03 08:59:18 UTC (rev 68587)
@@ -22,13 +22,14 @@
 will contain the probability values linked to these classifications 
 (<em>output_prob_column</em> - see below).
 
-<p>Different classifiers are proposed: k-nearest neighbor (knn and knn1 
-for k=1), support vector machine with a radial kernel (svmRadial), random 
-forest (rf) and recursive partitioning (rpart). Each of these classifiers
-is tuned automatically throught repeated cross-validation. caret will 
-automatically determine a reasonable set of values for tuning. See the 
-<a href="http://topepo.github.io/caret/modelList.html">caret webpage</a> for
-more information about the tuning parameters for each classifier, and
+<p>Different classifiers are proposed <em>classifiers</em>: 
+k-nearest neighbor (knn and knn1 for k=1), support vector machine 
+with a radial kernel (svmRadial), random forest (rf) and recursive 
+partitioning (rpart). Each of these classifiers is tuned automatically 
+throught repeated cross-validation. caret will automatically determine 
+a reasonable set of values for tuning. See the 
+<a href="http://topepo.github.io/caret/modelList.html">caret webpage</a> 
+for more information about the tuning parameters for each classifier, and
 more generally for the information about how caret works.
 
 <p>The user can chose to include the individual classifiers results in
@@ -38,15 +39,15 @@
 (<em>weighting_mode</em>): simple majority vote without weighting, i.e. 
 all weights are equal (smv), simple weighted majority vote (swv), 
 best-worst weighted vote (bwwv) and quadratic best-worst weighted vote 
-(qbwwv). For more details about these voting modes see [TODO: include 
-reference]. By default, the weights are calculated based on the accuracy 
+(qbwwv). For more details about these voting modes see Moreno-Seco et al 
+(2006). By default, the weights are calculated based on the accuracy 
 metric, but the user can chose the kappa value as an alternative 
 (<em>weighting_metric</em>).
 
 <p>In the output (as attribute columns or text file) each weighting schemes 
 result is provided accompanied by a value that can be considered as an
 estimation of the probability of the classification after weighted vote, 
-based on the equation used in [TODO: include reference].
+based on equation (2) in Moreno et al (2006), page 709.
 
 <p>Optional output of the module include a box-and-whisker plot indicating
 the resampling variance based on the cross-validation for each classifier 
@@ -54,6 +55,7 @@
 accuracy and kappa) for each classifier (<em>accuracy_file</em>). The user
 can also chose to write the R script constructed and used internally to a text
 file for study or further modification.
+
 <h2>NOTES</h2>
 
 <p>
@@ -89,6 +91,10 @@
 v.class.mlR segments_file=segstats.csv training_file=training.csv train_class_column=class weighting_mode=smv,swv,qbwwv raster_segments_map=seg classified_map=vote classification_results=class_results.csv
 </pre></div>
 
+<h2>REFERENCES</h2>
+
+<p>Moreno-Seco, F. et al. (2006), Comparison of Classifier Fusion Methods for Classification in Pattern Recognition Tasks. In D.-Y. Yeung et al., eds. Structural, Syntactic, and Statistical Pattern Recognition. Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 705–713, <a href="http://dx.doi.org/10.1007/11815921_77">http://dx.doi.org/10.1007/11815921_77</a>. 
+
 <h2>SEE ALSO</h2>
 
 <em>

Modified: grass-addons/grass7/vector/v.class.mlR/v.class.mlR.py
===================================================================
--- grass-addons/grass7/vector/v.class.mlR/v.class.mlR.py	2016-06-03 02:46:29 UTC (rev 68586)
+++ grass-addons/grass7/vector/v.class.mlR/v.class.mlR.py	2016-06-03 08:59:18 UTC (rev 68587)
@@ -12,7 +12,13 @@
 #               for details.
 #
 #############################################################################
-
+# References:
+#Moreno-Seco, F. et al. (2006), Comparison of Classifier Fusion Methods for
+#Classification in Pattern Recognition Tasks. In D.-Y. Yeung et al., eds.
+#Structural, Syntactic, and Statistical Pattern Recognition. Lecture Notes in
+#Computer Science. Springer Berlin Heidelberg, pp. 705–713, 
+#http://dx.doi.org/10.1007/11815921_77</a>.
+#############################################################################
 #%module
 #% description: Provides supervised support vector machine classification
 #% keyword: classification