ICS 269, Spring 1997: Theory Seminar
18 Apr 1997:
Learning Classifications via Generalized Linear Models
Jonathan Martin, ICS, UC Irvine
Though widely used in statistics and the social sciences,
regression models have been overlooked or rejected by the machine
learning field in favor of decision trees or neural networks. The
experimental results to be reported here show that generalized
linear models are an effective machine learning tool and that the
classifiers inferred via these models are frequently much more
accurate than those inferred by conventional decision tree methods.