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两个神经网络优化准则及其在无限制字符识别中的应用
Two Optimization Criterions for Neural Networks and Their Applications in Unconstrained Handwritten Character Recognition
【摘要】 本文根据信息理论提出了模式特征有效提取准则和稀疏表象准则两个新的神经网络优化准则,探讨了它们之间的相互关系,并给出了具体的能量函数形式和优化算法。设计了两个具体的神经网络模型,根据上述算法在无限制手写体字符识别的初步应用中得到了很好的结果。
【Abstract】 :Two new optimization criterions for neuro-computiong,Effective Feature Extraction rule(EFE-rule)and Sparse Representation Rule (SP-rule)respectively, are proposed based on recent progress in neural information processing.Possible cost function and learning algorithm are formulated.Two neural networks are constructed and good results on recognition of unconstrained handwritten characters are obtained.
【关键词】 特征有效提取准则;
稀疏表象准则;
能量函数;
神经网络;
字符识别;
【Key words】 :EFE-rule; SP-rule; Cost function; Neural networks; Character recognition.;
【Key words】 :EFE-rule; SP-rule; Cost function; Neural networks; Character recognition.;
【基金】 国家自然科学基金,江苏省青年科技基金,国家模式识别实验室开放课题
- 【文献出处】 电路与系统学报 ,Journal of Circuits and Systems , 编辑部邮箱 ,1997年03期
- 【分类号】TP391.4
- 【被引频次】2
- 【下载频次】73