¡¡Chinese Journal of Computers   Full Text
  TitlePattern Recognition Based on the Fuzzy Kernel Matching Pursuit
  AuthorsLI Qing1)£¬2) JIAO Li-Cheng2) ZHOU Wei-Da2)
  Address1)(Nanjing Institute of Electronic Technology, Nangjing 210013) 2)(Institute of Intelligent Information Processing, Xidian University, Xi¡äan 710071)
  Year2009
  IssueNo.8(1687¡ª1694)
  Abstract &
  Background 		Abstract Kernel Matching Pursuit (KMP), a novel method of the pattern recognition, presents excellent performance in solving the problems with small sample, nonlinear and local minima. KMP has been proposed to provide a good generalization performance for both classes, yet the classification precision of some important data can¡¯t be classified precisely. Because the decision function found by KMP is the synthetic consideration results of all the data, it has greatly limited its use in many practical problems, such as time series identification and unbalanced data classification. In this paper, an fuzzy kernel matching pursuit machine is (FKMP) proposed, which can classify the appointed important samples much more precisely according to the predefined importance of the data. Lots of experiments have been given in the paper to prove the feasibility and validation of the fuzzy kernel matching pursuit machine.
Keywords machine learning; kernel matching pursuit; fuzzy kernel matching pursuit; time series identification; unbalanced data classification
Background The authors have made researches on many fields of the support vector machine, such as Linear programming support vector machine, kernel matching pursuit classifier ensemble, support vector regression based on unconstrained convex quadratic programming and so on, and have applied these methods to the field of SAR image processing, recognition of plane HRRP and many other fields. This paper belongs to the part of novel method of machine learning and focuses on proposing an fuzzy kernel matching pursuit machine (FKMP), which can classify the appointed important samples much more precisely according to the predefined importance of the data, so as to develop the practical applications of the KMP.