Nonlinear Classification Models

Max Kuhn and Kjell Johnson
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Max Kuhn: Pfizer Global Research and Development, Division of Nonclinical Statistics
Kjell Johnson: Arbor Analytics

Chapter Chapter 13 in Applied Predictive Modeling, 2013, pp 329-367 from Springer

Abstract: Abstract Chapter 12 discussed classification models that defined linear classification boundaries. In this chapter we present models that generate nonlinear boundaries. We begin with explaining several generalizations to the linear discriminant analysis framework such as quadratic discriminant analysis, regularized discriminant analysis, and mixture discriminant analysis (Section 13.1). Other nonlinear classification models include neural networks (Section 13.2), flexible discriminant analysis (Section 13.3), support vector machines (Section 13.4), K-nearest neighbors (Section 13.5), and naive Bayes (Section 13.6). In the Computing Section (13.7) we demonstrate how to train each of these models in R. Finally, exercises are provided at the end of the chapter to solidify the concepts.

Keywords: Support Vector Machine; Linear Discriminant Analysis; Hide Unit; Classification Boundary; Chief Investigator (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4614-6849-3_13

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DOI: 10.1007/978-1-4614-6849-3_13

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