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Abstract:
We describe an iterative algorithm for building vector
machines used in classification tasks. The algorithm builds on
ideas from support vector machines, boosting, and generalized
additive models. The algorithm can be used with various
continuously differential functions that bound the discrete (0-1)
classification loss and is very simple to implement. We test the
proposed algorithm with two different loss functions on synthetic
and natural data. We also describe a norm-penalized version of the
algorithm for the exponential loss function used in AdaBoost. The
performance of the algorithm on natural data is comparable to
Support Vector machines while typically both its running time is
shorter than of SVM and the sizes of the final classifiers it
builds are smaller.
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