TY - GEN
T1 - Margin Analysis of the LVQ Algorithm
AU - Crammer, Koby
AU - Gilad-Bachrach, Ran
AU - Navot, Amir
AU - Tishby, Naftali
N1 - Publisher Copyright:
© NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems. All rights reserved.
PY - 2002
Y1 - 2002
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85156210264&partnerID=8YFLogxK
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AN - SCOPUS:85156210264
T3 - NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems
SP - 462
EP - 469
BT - NIPS 2002
A2 - Becker, Suzanna
A2 - Thrun, Sebastian
A2 - Obermayer, Klaus
PB - MIT Press
T2 - 15th International Conference on Neural Information Processing Systems, NIPS 2002
Y2 - 9 December 2002 through 14 December 2002
ER -