【摘要】 提出了一种基于仿射传播聚类(Affinity Propagation Clustering,AP Clustering)和仿生模式识别理论(Biomi-metic Pattern Recognition,BPR)的识别方法。该方法通过AP聚类选择代表训练样本,依据仿生模式识别理论构建并划分样本空间,通过计算待识样本到各特征子空间的相对距离,根据其所处空间进行分类识别。在因空间重叠造成拒识的情况下,通过计算基于类条件的后验概率对样本进行相对区别。在Concordia大学CENPARMI手写体数字库与南京理工大学手写金额库上进行了实验,结果表明,该方法在识别率方面优于传统的分类器。更多还原

【Abstract】 A classify based on AP Clustering and biomimetic pattern recognition was proposed.It can relatively classify the samples by calculating the distance to the relative subspace.The training sample space was constructed by the AP algorithm and bionic pattern recognition theory.The posterior probabilities based on the class condition were estimated to reduce the reject rate caused by the space overlapping with low misclassification.Experiments were performed with Concordia University CENPARMI’s handwritten digit database and Nanjing University of Science and Technology’s handwritten amount database.Experimental results indicate that the proposed classifier has a higher recognition rate than the traditional classifiers.更多还原