Title :
Bayesian classifier based on discretized continuous feature space
Author :
Zhou, Dequan ; Wu, Liguang ; Liu, GuoSui
Author_Institution :
Air Force No.1 Inst. of Aeronaut., Xinyang City, China
Abstract :
Bayesian decision theory is widely used in pattern recognition and signal detection. Only when the class-conditional-probability density is known can the theory be used. A discretization method of stochastic variable (feature) space of the class-conditional-probability-density, and an estimation method for the class-conditional-probability-distribution are proposed. A Bayesian classification algorithm based on the methods is given. Finally, the methods are illustrated by applying them to radar target recognition
Keywords :
Bayes methods; decision theory; pattern classification; probability; radar signal processing; radar target recognition; stochastic processes; Bayesian classifier; class-conditional-probability density; class-conditional-probability-distribution; discretization method; discretized continuous feature space; estimation method; pattern recognition; radar target recognition; signal detection; stochastic variable space; Bayesian methods; Function approximation; Multi-layer neural network; Neural networks; Pattern recognition; Probability density function; Space technology; Statistical distributions; Statistics; Stochastic processes;
Conference_Titel :
Signal Processing Proceedings, 1998. ICSP '98. 1998 Fourth International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-4325-5
DOI :
10.1109/ICOSP.1998.770839
