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Learning with Threshold Decision Lists

Martin Anthony Department of Mathematics London School of Economics 4pm 27 April 2004 Room 2511, JCMB, King's Buildings

We consider pattern classification methods based on the iterative use of linear classifiers. The resulting classifiers, called threshold decision lists, act as follows. Some points of the data set to be classified are given a particular classification according to a linear threshold function (or hyperplane). These are then removed from consideration, and the procedure is iterated until all points are classified.

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