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An Evolving System in the Text Classification Problem
Abstract
Traditional machine learning techniques have been successful in yielding good results when the data are stable along the time horizon. However, in many cases, these techniques may be inefficient for data that are constantly expanding and changing over time. To address this problem, new learning techniques have been proposed in the literature. In this chapter, the authors discuss some improvements on their technique, called Evolving Probabilistic Neural Network (ePNN), and present the aspects of this recent learning paradigm. This technique is based on the Probabilistic Neural Networks. In this chapter the authors compare their technique against two other competitive techniques that can be found in the literature: Incremental Probabilistic Neural Network (IPNN) and Evolving Fuzzy Neural Network (EFuNN). To show the better performance of their technique, the authors present and discuss a series of experiments that demonstrate the efficiency of ePNN over both the IPNN and EFuNN approaches.
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