Learning pattern classification-a survey

  • Authors:
  • S. R. Kulkarni;G. Lugosi;S. S. Venkatesh

  • Affiliations:
  • Dept. of Electr. Eng., Princeton Univ., NJ;-;-

  • Venue:
  • IEEE Transactions on Information Theory
  • Year:
  • 2006

Quantified Score

Hi-index 754.84

Visualization

Abstract

Classical and recent results in statistical pattern recognition and learning theory are reviewed in a two-class pattern classification setting. This basic model best illustrates intuition and analysis techniques while still containing the essential features and serving as a prototype for many applications. Topics discussed include nearest neighbor, kernel, and histogram methods, Vapnik-Chervonenkis theory, and neural networks. The presentation and the large (though nonexhaustive) list of references is geared to provide a useful overview of this field for both specialists and nonspecialists