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IJON
2006
99views more  IJON 2006»
13 years 7 months ago
Learning vector quantization: The dynamics of winner-takes-all algorithms
Winner-Takes-All (WTA) prescriptions for Learning Vector Quantization (LVQ) are studied in the framework of a model situation: Two competing prototype vectors are updated accordin...
Michael Biehl, Anarta Ghosh, Barbara Hammer
NIPS
2000
13 years 9 months ago
Incremental and Decremental Support Vector Machine Learning
An on-line recursive algorithm for training support vector machines, one vector at a time, is presented. Adiabatic increments retain the KuhnTucker conditions on all previously se...
Gert Cauwenberghs, Tomaso Poggio
FORMATS
2004
Springer
14 years 1 months ago
Learning of Event-Recording Automata
Abstract. We extend Angluin’s algorithm for on-line learning of regular languages to the setting of timed systems. We consider systems that can be described by a class of determi...
Olga Grinchtein, Bengt Jonsson, Martin Leucker
COLT
2008
Springer
13 years 9 months ago
More Efficient Internal-Regret-Minimizing Algorithms
Standard no-internal-regret (NIR) algorithms compute a fixed point of a matrix, and hence typically require O(n3 ) run time per round of learning, where n is the dimensionality of...
Amy R. Greenwald, Zheng Li, Warren Schudy
COLT
2001
Springer
14 years 5 days ago
Smooth Boosting and Learning with Malicious Noise
We describe a new boosting algorithm which generates only smooth distributions which do not assign too much weight to any single example. We show that this new boosting algorithm ...
Rocco A. Servedio