ftp-papers:learn,tune

[Grace Wahba]

 The following technical reports are available by anonymous ftp.

nonlin-learn.ps.Z
       G. Wahba, Generalization and Regularization in Nonlinear
       Learning Systems, UW-Madison Statistics Dept TR 921, 
       May, 1994. To appear in the Handbook of Brain Theory, 
       Michael Arbib, Ed. 
          Relates feedforward neural nets, radial basis 
	  functions and smoothing spline anova within the length 
	  limitations of the Handbook.

tuning-nwp.ps.Z 
       G. Wahba, D. R. Johnson, F. Gao and J. Gong, Adaptive tuning 
       of numerical weather prediction models:  Part I: randomized 
       GCV and related methods in three and four dimensional 
       data assimilation. UW-Madison Statistics Dept TR 920, 
       April, 1994, submitted.
           Shows how to tune the bias-variance tradeoff and 
	   other tradeoffs via generalized cross validation 
	   (gcv), unbiased risk (ubr), and generalized maximum 
	   likelihood (gml) with very large data sets in the 
	   context of regularized function estimation.
	   Shows how to use randomized trace estimation to 
	   compute gcv and ubr, and in particular to use 
	   these randomized estimates to estimate when to 
	   stop the iteration when large variational problems
	   are solved iteratively. Written in the language 
	   of data assimilation in numerical weather prediction
	   but the methods may be of interest in machine learning.
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ftp instructions:  fn = nonlin-learn or tuning-nwp 

% ftp ftp.stat.wisc.edu
Name: anonymous
password: your email address
ftp> cd pub/wahba
ftp> binary
ftp> get fn.ps.Z
ftp> bye
% uncompress fn.ps.Z
% lpr fn.ps
-------------------------------------------------------------------- 
  Grace Wahba
  Statistics Dept
  University of Wisconsin-Madison
 
  wahba at stat.wisc.edu
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