BIB-VERSION:: CS-TR-v2.0
ID:: ncstrl.dartmouthcs//TR2002-428
ENTRY:: June 12, 2002
ORGANIZATION:: Dartmouth College, Computer Science
TITLE:: Information-theoretic Bounds on the Training and Testing Error of Boosting
TYPE:: Technical Report (paper)
REVISION:: 1
AUTHOR:: Lahaie, Sebastien M. 
DATE:: May 2002
RETRIEVAL:: For a paper copy, email <reports@cs.dartmouth.edu>
RETRIEVAL:: For a paper copy, write to
       Technical Report Librarian
       Department of Computer Science
       Dartmouth College
       6211 Sudikoff Laboratory
       Hanover, NH 03755-3510
       USA
RETRIEVAL:: Compressed Postscript at http://www.cs.dartmouth.edu/reports/TR2002-428.ps.Z
RETRIEVAL:: PDF at http://www.cs.dartmouth.edu/reports/TR2002-428.pdf
ABSTRACT:: 
Boosting is a means of using weak learners as subroutines to produce a
strong learner with markedly better accuracy. Recent results showing
the connection between logistic regression and boosting provide the
foundation for an information-theoretic analysis of boosting. We
describe the analogy between boosting and gambling, which allows us to
derive a new upper bound on training error. This upper bound
explicitly describes the effect of noisy data on training error. We
also use information-theoretic techniques to derive an alternative upper-bound
on testing error which is independent of the size of the weak-learner
space.
END:: ncstrl.dartmouthcs//TR2002-428