Titlebook: Data Analytics; Models and Algorithm Thomas A. Runkler Textbook 20121st edition Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden 2012

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https://doi.org/10.1007/978-981-19-0549-0re presented in detail: the naive Bayes classifier, linear discriminant analysis, the support vector machine (SVM) using the kernel trick, nearest neighbor classifiers, learning vector quantification, and hierarchical classification using regression trees.

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The Circular Economy and Business Challengeseptron and radial basis function networks. Universal approximators can realize arbitrarily small training errors, but cross-validation is required to find models with low validation errors that generalize well on other data sets. Feature selection allows us to include only relevant features in regression models leading to more accurate models.

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sed for more than 10 years.Includes supplementary material: This book is a comprehensive introduction to the methods and algorithms and approaches of modern data analytics. It covers data preprocessing, visualization, correlation, regression, forecasting, classification, and clustering. It provides