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TitleData mining with multilayer perceptrons and support vector machines
Author(s)Cortez, Paulo
KeywordsData mining
Neural networks
Support vector machines
Issue date2012
PublisherSpringer Verlag
JournalIntelligent Systems Reference Library
Abstract(s)Multilayer perceptrons (MLPs) and support vector machines (SVMs) are flexible machine learning techniques that can fit complex nonlinear mappings. MLPs are the most popular neural network type, consisting on a feedforward network of processing neurons that are grouped into layers and connected by weighted links. On the other hand, SVM transforms the input variables into a high dimensional feature space and then finds the best hyperplane that models the data in the feature space. Both MLP and SVM are gaining an increase attention within the data mining (DM) field and are particularly useful when more simpler DM models fail to provide satisfactory predictive models. This tutorial chapter describes basic MLP and SVM concepts, under the CRISP-DM methodology, and shows how such learning tools can be applied to real-world classification and regression DM applications. © Springer-Verlag Berlin Heidelberg 2012.
TypeBook part
Publisher version
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Livros e capítulos de livros/Books and book chapters
DSI - Engenharia da Programação e dos Sistemas Informáticos

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