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» The Inefficiency of Batch Training for Large Training Sets
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JMLR
2006
89views more  JMLR 2006»
13 years 8 months ago
Maximum-Gain Working Set Selection for SVMs
Support vector machines are trained by solving constrained quadratic optimization problems. This is usually done with an iterative decomposition algorithm operating on a small wor...
Tobias Glasmachers, Christian Igel
AAAI
2011
12 years 8 months ago
An Online Spectral Learning Algorithm for Partially Observable Nonlinear Dynamical Systems
Recently, a number of researchers have proposed spectral algorithms for learning models of dynamical systems—for example, Hidden Markov Models (HMMs), Partially Observable Marko...
Byron Boots, Geoffrey J. Gordon
ICML
2005
IEEE
14 years 9 months ago
Large scale genomic sequence SVM classifiers
In genomic sequence analysis tasks like splice site recognition or promoter identification, large amounts of training sequences are available, and indeed needed to achieve suffici...
Bernhard Schölkopf, Gunnar Rätsch, S&oum...
KDD
2002
ACM
138views Data Mining» more  KDD 2002»
14 years 9 months ago
Learning to match and cluster large high-dimensional data sets for data integration
Part of the process of data integration is determining which sets of identifiers refer to the same real-world entities. In integrating databases found on the Web or obtained by us...
William W. Cohen, Jacob Richman
ICPR
2006
IEEE
14 years 9 months ago
Bagging Based Efficient Kernel Fisher Discriminant Analysis for Face Recognition
Kernel Fisher Discriminant Analysis (KFDA) has achieved great success in pattern recognition recently. However, the training process of KFDA is too time consuming (even intractabl...
Baochang Zhang, Shiguang Shan, Wen Gao, Xilin Chen...