摘要
分析了训练样本对于识别系统性能的影响,将训练样本分为三种:好样本、差样本和边界样本,分析了它们在训练中所起的作用,并结合基于HMM的手写数字识别系统,给出了一种简单的边界样本定义和选择的方法;通过实验证明了采用边界样本训练可使系统误识率降低17.51%,并证明了边界样本的重要性,且指出非边界样本的存在会影响训练的效果.
In the paper, the influence of training sample to the performance of recognition system is analyzed. The training samples are classified into three classes： good sample, poor sample and boundary sample. Combined with the handwritten numeral recognition system based on HMM, a simple method of definition and selection of boundary sample is given. The experimental result shows the miss-recognition rate is reduced by 17.51% by introducing boundary sample training, which verify the importance of boundary sample, the existence of non-boundary sample will influence the training effects.
出处
《计算机学报》
EI
CSCD
北大核心
2005年第11期1923-1928,共6页
Chinese Journal of Computers
基金
国家"八六三"高技术研究发展计划项目基金(2001AA114080)
国家自然科学基金(60475007)资助
关键词
训练样本
样本分类
边界样本
HMM
training sample
sample classifying
boundary sample
HMM
