Margin analysis of the LVQ algorithm

Koby Crammer, Ran Gilad-Bachrach, Amir Navot, Naftali Tishby

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Prototypes based algorithms are commonly used to reduce the computational complexity of Nearest-Neighbour (NN) classifiers. In this paper we discuss theoretical and algorithmical aspects of such algorithms. On the theory side, we present margin based generalization bounds that suggest that these kinds of classifiers can be more accurate then the 1-NN rule. Furthermore, we derived a training algorithm that selects a good set of prototypes using large margin principles. We also show that the 20 years old Learning Vector Quantization (LVQ) algorithm emerges naturally from our framework.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002
PublisherNeural information processing systems foundation
ISBN (Print)0262025507, 9780262025508
StatePublished - 2003
Externally publishedYes
Event16th Annual Neural Information Processing Systems Conference, NIPS 2002 - Vancouver, BC, Canada
Duration: 9 Dec 200214 Dec 2002

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Conference16th Annual Neural Information Processing Systems Conference, NIPS 2002
CityVancouver, BC


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