Bias-Variance, GACV paper

[Grace Wahba]

The following paper has been accepted for oral
presentation at NIPS*98:  Available as University
of Wisconsin-Madison Statistics Dept TR997 in 

http://www.stat.wisc.edu/~wahba -> TRLIST
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The Bias-Variance Tradeoff and the Randomized GACV

Grace Wahba*, Xiwu Lin, Fangyu Gao,  Dong Xiang, 
Ronald Klein MD and Barbara Klein MD


We propose a new in-sample cross validation based 
method (randomized GACV) for choosing smoothing 
or bandwidth parameters that govern the bias-variance 
or fit-complexity tradeoff in `soft' classification. 
Soft classification refers to a learning procedure 
which estimates the probability
that an example with a given attribute vector is in 
class 1 {\it vs} class 0. The target for optimizing the 
the tradeoff is the Kullback-Liebler distance between 
the estimated probability distribution and the `true' 
probability distribution, representing knowledge 
of an infinite population. The method uses a 
randomized estimate of the trace of a Hessian and mimics 
cross validation at the cost of a single relearning with 
perturbed outcome data.

*corresponding author wahba at stat.wisc.edu 
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