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IPSN
2010
Springer
15 years 4 months ago
Consensus-based distributed linear support vector machines
This paper develops algorithms to train linear support vector machines (SVMs) when training data are distributed across different nodes and their communication to a centralized no...
Pedro A. Forero, Alfonso Cano, Georgios B. Giannak...
NIPS
2000
14 years 11 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
ICDM
2006
IEEE
108views Data Mining» more  ICDM 2006»
15 years 3 months ago
Minimum Enclosing Spheres Formulations for Support Vector Ordinal Regression
We present two new support vector approaches for ordinal regression. These approaches find the concentric spheres with minimum volume that contain most of the training samples. B...
Shirish Krishnaj Shevade, Wei Chu
IJON
2008
101views more  IJON 2008»
14 years 9 months ago
Learning dynamics and robustness of vector quantization and neural gas
Various alternatives have been developed to improve the Winner-Takes-All (WTA) mechanism in vector quantization, including the Neural Gas (NG). However, the behavior of these algo...
Aree Witoelar, Michael Biehl, Anarta Ghosh, Barbar...
SDM
2007
SIAM
81views Data Mining» more  SDM 2007»
14 years 11 months ago
A PAC Bound for Approximate Support Vector Machines
We study a class of algorithms that speed up the training process of support vector machines (SVMs) by returning an approximate SVM. We focus on algorithms that reduce the size of...
Dongwei Cao, Daniel Boley