\section{Discussion}

dear all, this is a dumping ground of stuff that i plan to get too... maybe

TEAK works surprisingly well. 
why does it work. recall the example of \tion{remove}.


focusing on the easy case is the opposite to focusing on the hard case, 
as done in boosting. our prior experiments with boosting~\cite{baker07} lead to disappointing results. 
Booting: focus on the hard case. Here, we focus on the east case

other options: 
 Later in this paper, we discuss other design options such as
scaling measures, feature weighting, and similarity measures.

the myreveit result
low variance in result.
first, reduce the variance, then reason
first reduce the variance, then perform the evaluation


These results echo early work by
Kadoda and Shepperd~\cite{Kadoda2001} 
who
compared the effort models learned 
from a variant of regression, rule induction, case-based
reasoning (CBR), and neural networks. 
They showed that any conclusion about the relative value of these four methods
differed markedly across different data sets.


Also, there is an interesting quirk in 
their experimental results.
Estimation methods can
{\em prune} tables of training data:
\bi
\item
CBR prunes away irrelevant {\em rows};
\item
Stepwise regression prunes away {\em columns} that
add little information to the regression equation;
\item
The Shepperd \& Kadoda's rule induction and neural net instantiations
have no row/column pruning.
\ei
Note that the two methods found to be
``worst'' by Shepperd \& Kadoda had no
row or column pruning methods.
Pruning data can be useful to remove outliers or ``noisy'' information
(spurious signals unconnected to the target variable). One symptom of outliers and noise is 
conclusion instability across different data sets and different random samplings.
Hence, we wondered if conclusion instability could be tamed via the application of more elaborate row {\em
and} column pruners.


\section{Future Work: TEAK (version two)}

feature subset selection, feature weighting, similarity measures, etc.

