A mathematical framework for the analysis of critical zones of the input space in a classification problem is introduced. It is based on the definition of uncertainty region, which is the collection of the input patterns whose classification is not certain. Through this definition a characterization of optimal decision functions can be derived. A general method for detecting the uncertainty region in real-world problems is then proposed, whose implementation can vary according to the connectionist model employed. Its application allows to improve the performance of the resulting neural network.
Detecting uncertainty regions for characterizing classification problems
M Muselli
2002
Abstract
A mathematical framework for the analysis of critical zones of the input space in a classification problem is introduced. It is based on the definition of uncertainty region, which is the collection of the input patterns whose classification is not certain. Through this definition a characterization of optimal decision functions can be derived. A general method for detecting the uncertainty region in real-world problems is then proposed, whose implementation can vary according to the connectionist model employed. Its application allows to improve the performance of the resulting neural network.File in questo prodotto:
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