Bayesian classifier based on discretized continuous feature space
Dequan Zhou, Liguang Wu, Liu Guosui · 2002
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.