@inproceedings {pub2594,
	title = {Local Rejection Strategies for Learning Vector Quantization},
	author = {Lydia Fischer AND Barbara Hammer AND Heiko Wersing},
	year = {2014},
	month = {September},
	abstract = {Classication with rejection is well understood for classiers
which provide explicit class probabilities. The situation is more complicated
for popular deterministic classiers such as learning vector quantisation
schemes: albeit reject options using simple distance-based geometric
measures were proposed [4], their local scaling behaviour is unclear
for complex problems. Here, we propose a local threshold selection
strategy which automatically adjusts suitable threshold values for reject
options in prototype-based classiers from given data. We compare this
local threshold strategy to a global choice on articial and benchmark
data sets; we show that local thresholds enhance the classication results
in comparison to global ones, and they better approximate optimal
Bayesian rejection in cases where the latter is available.},
	publisher = {Springer},
	booktitle = {Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks},
	editor = {Stefan Wermter and Cornelius Weber and Wlodzislaw Duch and Timo Honkela and Petia D. Koprinkova-Hristova and Sven Magg and Guenther Palm and Alessandro E. P. Villa},
	city = {Hamburg, Germany},
	volume = {8681},
	pages = {563-570},
	series = {Lecture Notes in Computer Science}
}