Pruning and Regularization Techniques for Feed Forward Nets Applied on a Real World Data Base
نویسندگان
چکیده
In this paper we present an extensive study of weight pruning and regularization techniques for feed forward neural nets. This algorithm comparison is based on a data base of known benchmark data sets and a realistic real world data set from automotive control. We trained 30 nets for every algorithm implemented by us, with three different ways of data set splitting. For the 38 implemented methods this results into totally 1140 trained nets.
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