Add description for SVM

mini_proj/report/references.bib

@@ -1,8 +1,8 @@
-@misc{openData, 
+@misc{openData,
-  title={Open Database License (ODbL) v1.0}, 
+  title={Open Database License (ODbL) v1.0},
-  url={https://opendatacommons.org/licenses/odbl/1.0/}, 
+  url={https://opendatacommons.org/licenses/odbl/1.0/},
-  journal={Open Data Commons}, 
+  journal={Open Data Commons},
-  year={2018}, 
+  year={2018},
   month={Feb}
 }
 @techreport{knn,
@@ -21,6 +21,14 @@
   year={1995},
   publisher={Springer}
 }
+@inproceedings{svmnonlinear,
+  title={A training algorithm for optimal margin classifiers},
+  author={Boser, Bernhard E and Guyon, Isabelle M and Vapnik, Vladimir N},
+  booktitle={Proceedings of the fifth annual workshop on Computational learning theory},
+  pages={144--152},
+  year={1992},
+  organization={ACM}
+}
 @article{naivebayes,
   title={Idiot's Bayes—not so stupid after all?},
   author={Hand, David J and Yu, Keming},

mini_proj/report/waldo.tex

@@ -142,7 +142,15 @@
 
 		\paragraph{Support Vector Machine}
 
-		\cite{svm}
+		(SVM) \cite{svm} has been very successful in many classification tasks. The
+		method is based on finding boundaries between the different classes. The
+		boundaries are defined as functions on the features of the instances. The
+		boundaries are optimized to have the most amount of space between the
+		boundaries and the training instances on both sides. Originally the
+		boundaries where linear functions, but more recent development allows for
+		the training of non-linear boundaries~\cite{svmnonlinear}. Once the training
+		has defined the boundaries new instances are classified according to on
+		which side of the boundary they belong.
 
 		\paragraph{Random Forest}