add reference for cross validation

30_Thesis/thesis.bib

@@ -1005,6 +1005,16 @@ procedure.},
   langid = {english}
 }
 
+@inproceedings{kohaviStudyCrossValidationBootstrap1995,
+  title = {A {{Study}} of {{Cross}}-{{Validation}} and {{Bootstrap}} for {{Accuracy Estimation}} and {{Model Selection}}},
+  author = {Kohavi, Ron},
+  date = {1995},
+  pages = {1137--1143},
+  publisher = {{Morgan Kaufmann}},
+  abstract = {We review accuracy estimation methods and compare the two most common methods: crossvalidation and bootstrap. Recent experimental results on artificial data and theoretical results in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), ten-fold cross-validation may be better than the more expensive leaveone -out cross-validation. We report on a largescale experiment---over half a million runs of C4.5 and a Naive-Bayes algorithm---to estimate the effects of different parameters on these algorithms on real-world datasets. For crossvalidation, we vary the number of folds and whether the folds are stratified or not; for bootstrap, we vary the number of bootstrap samples. Our results indicate that for real-word datasets similar to ours, the best method to use for model selection is ten-fold stratified cross validation, even if computation power allows using more folds. 1 Introduction It can not be emphasized enough that no claim ...},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\GGH5NYBZ\\Kohavi_1995_A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model.pdf;C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\M7BT7CCG\\summary.html}
+}
+
 @online{kuchelmeister13hannes11BachelorThesis,
   title = {13hannes11/Bachelor\_thesis\_m.Recommend},
   author = {Kuchelmeister, Hannes F.},