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Linear Classifiers for Nonseparable Classes

  • Jack Sklansky
  • Gustav N. Wassel

Abstract

In this chapter we describe several training procedures based on the gradient and stochastic approximation techniques of the preceding chapter. Although our discussion in this chapter is restricted to two-class cases, the techniques may be extended to multiple-class cases by using the concepts of Section 2.6. The training procedures of this chapter apply to pairs of classes that are linearly nonseparable, i.e., that are not linearly separable, as well as those that are linearly separable. Linearly nonseparable classes include pairs of classes that are separable but not by a hyperplane, as well as pairs of classes that overlap.

Keywords

Feature Vector Weight Vector Training Procedure Initial Vector Linear Classifier 
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 New York Inc 1981

Authors and Affiliations

  • Jack Sklansky
    • 1
  • Gustav N. Wassel
    • 2
  1. 1.Department of Electrical EngineeringUniversity of California at IrvineIrvineUSA
  2. 2.Deparment of Electronic and Electrical EngineeringCalifornia Polytechnic State UniversitySan Luis ObispoUSA

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