%\subsection{Sch\"{o}lkopf Results}


%Parameters for Scholkopf:
%1.Scholkopf using 10 features is best for SVM.
%2.Scholkopf using Binary is best (for 20 features) (and Malik claims
%doesnt matter if 10 or 20 or whatever.)  Moreover, VERY BAD using
%Tf-idf or Hadamard.   Need explanation as to why so sensitive to 
%representation. 
%3. Scholkopf VERY sensitive to Kernel choice, even within BINARY.
%Moreover, changed between 20 and others for Radial basis is very 
%dramatic.    No explanation.   Malik is double checking the results.

%FINAL CHOICE:  Scholkopf with 10,or 20 but use linear probably, definitely
%with BINARY.

%%%THE NEXT TWO TABLES ARE ELIMINATED ON AUG 29 I HOPE
%\input { table-svm-oneclass-m10.tex }
%\input {table-svm-oneclass-m20.tex }

\input {table-svm-oneclass-all.tex}
%\input {table-svm-oneclass-m40.tex}
%\input {table-svm-oneclass-m60.tex}
%\input {table-svm-oneclass-m100.tex}

\input { table-svm-oneclass-hadamard.tex}
\input {table-svm-oneclass-tfidf.tex}
\input {table-svm-oneclass-freq.tex}




%\subsection{Outlier Results}
%Parameters for Outliers
%1. Used best Hamming 7/20.  
%2. Polynomial poor; other kernels compable.

%Sholkopf seems superior to outliers for optimal choices.

\input {table-svm-split.tex}
\input {table-svm-bin.tex}
\input {table-svm-optimum.tex}

\input{table-svm-outlier-linear-10a.tex}
\input{table-svm-outlier-poly-10a.tex}
\input{table-svm-outlier-radial-10a.tex}
\input{table-svm-outlier-sigmoid-10a.tex}
%\subsection{Neural Network Results}

%\subsection{Other Methods Results}

\input{table-main-comp.tex}

\subsection{Comparisons and Conclusions}

Looking over table, xxx, we see that the One-class SVM as proposed
by Sch\"{o}lkopf and all, gives the best performance.   This is quite
clear with respect to all the other algorithms except the compression
NN algorithm which seems comparable.
This method
also has the usual advantages of SVM; in particular it is less 
computationally intensive than Neural networks.

On the other hand, the  one class SVM  was very sensitive to the parameters
and choice of kernel.    The neural network method, in comparison,
seemed relatively stable over these parameters.    Thus, under current
knowledge, i.e. until understanding of the parameter choice is clearer,
it would seem that the NN method  is the preferred one.


%Parameters and Results for Nearest Neighbor:




%Parameters and Results for Rochio:


%Bayes-Parameters




%Parameters and Results for Neural Networks: