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Classification

  • Ron WehrensEmail author
Chapter
  • 5.8k Downloads
Part of the Use R book series (USE R)

Abstract

The goal of classification, also known as supervised pattern recognition, is to provide a model that yields the optimal discrimination between several classes in terms of predictive performance. It is closely related to clustering. The difierence is that in classification it is clear what to look for: the number of classes is known, and the classes themselves are well-defined, usually by means of a set of examples, the training set. Labels of objects in the training set are generally taken to be error-free, and are typically obtained from information other than the data we are going to use in the model. For instance, one may have data – say, concentration levels of several hundreds of proteins in blood – from two groups of people, healthy, and not-so-healthy, and the aim is to obtain a classification model that distinguishes between the two states on the basis of the protein levels.

Keywords

Hide Layer Discriminant Analysis Linear Discriminant Analysis Gini Index Hide Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Research and Innovation CentreFondazione Edmund MachSan Michele all’AdigeItaly

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