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ICONIP
2007
13 years 9 months ago
Using Generalization Error Bounds to Train the Set Covering Machine
In this paper we eliminate the need for parameter estimation associated with the set covering machine (SCM) by directly minimizing generalization error bounds. Firstly, we consider...
Zakria Hussain, John Shawe-Taylor
ESANN
2004
13 years 9 months ago
Evolutionary tuning of multiple SVM parameters
The problem of model selection for support vector machines (SVMs) is considered. We propose an evolutionary approach to determine multiple SVM hyperparameters: The covariance matr...
Frauke Friedrichs, Christian Igel
ICML
2003
IEEE
14 years 8 months ago
SimpleSVM
We present a fast iterative support vector training algorithm for a large variety of different formulations. It works by incrementally changing a candidate support vector set usin...
S. V. N. Vishwanathan, Alex J. Smola, M. Narasimha...
ICML
2007
IEEE
14 years 8 months ago
Fast and effective kernels for relational learning from texts
In this paper, we define a family of syntactic kernels for automatic relational learning from pairs of natural language sentences. We provide an efficient computation of such mode...
Alessandro Moschitti, Fabio Massimo Zanzotto
NECO
2008
112views more  NECO 2008»
13 years 7 months ago
Second-Order SMO Improves SVM Online and Active Learning
Iterative learning algorithms that approximate the solution of support vector machines (SVMs) have two potential advantages. First, they allow for online and active learning. Seco...
Tobias Glasmachers, Christian Igel