Tracking the Best Regressor

Tracking the Best Regressor (1998)

Cached

Download Links

[www.cse.ucsc.edu]
[www.cse.ucsc.edu]

Other Repositories/Bibliography

DBLP

by Mark Herbster , Manfred K. Warmuth

Venue:	In Proc. 11th Annu. Conf. on Comput. Learning Theory
Citations:	17 - 6 self

Versions

Version History

Metadata Version 3

User correction supplied by SystemCorrections

Datum	Value	Source
TITLE	Tracking the Best Regressor	user correction - Legacy Corrections
AUTHOR NAME	Mark Herbster	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Computer Science; University of California at Santa Cruz	SVM HeaderParse 0.2
AUTHOR ADDR	Applied Sciences Building; Santa Cruz, CA 95064 (USA)	SVM HeaderParse 0.1
AUTHOR NAME	Manfred K. Warmuth	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Computer Science; University of California at Santa Cruz	SVM HeaderParse 0.2
AUTHOR ADDR	Applied Sciences Building; Santa Cruz, CA 95064 (USA)	SVM HeaderParse 0.1
ABSTRACT	In most of the on-line learning research the total on-line loss of the algorithm is compared to the total loss of the best off-line predictor u from a comparison class of predictors. We call such bounds static bounds. The interesting feature of these bounds is that they hold for an arbitrary sequence of examples. Recently some work has been done where the comparison vector u t at each trial t is allowed to change with time, and the total online loss of the algorithm is compared to the sum of the losses of u t at each trial plus the total "cost" for shifting to successive comparison vectors. This is to model situations in which the examples change over time and different predictors from the comparison class are best for different segments of the sequence of examples. We call such bounds shifting bounds. Shifting bounds still hold for arbitrary sequences of examples and also for arbitrary partitions. The algorithm does not know the offline partition and the sequence of predictors that i...	user correction - Legacy Corrections
YEAR	1998	INFERENCE
VENUE	In Proc. 11th Annu. Conf. on Comput. Learning Theory	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	24--31	INFERENCE
CITATIONS	27 found	ParsCit 1.0