1、This paper presents an efficient training algorithm for probabilistic neural networks using the minimum classification error criterion.
2、In this paper, firstly a technique for confusion class recognition based on classification error distribution is proposed to recognize confusion class sets existing in the pre-defined taxonomy.
3、Learning rules are constructed according to deterministic annealing to optimize classifier parameters, on purpose to reduce classification error and system entropy of the space to be identified.
