| 摘要: |
| 针对在线手绘图符识别中样本训练存在的"收集足够数量模板或样本并保持其区分度"的难点,提出了一种适合于手绘图符识别的基于检测器生成的克隆选择算法及其评价方法.该算法采用r-连续位不变规则和p-受体编辑生成初始检测器,使算法具有更广泛的搜索空间并不致陷入局部收敛.以手写文字作为实验对象,评价该算法各参数对个体训练的影响,实验结果验证了该算法对手绘图符样本训练及分类的改进. |
| 关键词: 人工免疫模型 检测器 在线手绘图符 样本训练 连续位不变规则 受体编辑 高频变异 局部收敛 克隆选择算法 |
| DOI： |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.69903006, 60373065, 60721002 (国家自然科学基金); the National High-Tech Research and Development Plan of China under Grant No.2007AA01Z334 (国家高技术研究发展计划(863)); the Program for New Century Excellent Talents in University of China under Grant No.NCET-04-0460 (新世纪优秀人才资助计划) |
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| An Immune Clonal Selection Algorithm for Online Symbol Recognition |
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ZHANG Li-Sha,SUN Zheng-Xing
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| Abstract: |
| In order to collect sufficiently many samples and keep their distinguishability in online sketchy symbol recognition, this paper proposes a detector-generation based clonal selection algorithm and an evaluation method. The algorithm generates detectors with an r-contiguous-bits unchanged rule (r-CBUR) and a p-receptor editing to search in a wide feature space and try to avoid local convergences. Hand-Written Chinese characters are selected as experimental samples, for which the influence of the training parameters is analyzed. The experimental results show the improvements of the training process and the classification results of sketchy symbol recognition. |
| Key words: artificial immune model detector online sketchy symbol sample training contiguous-bits unchanged rule receptor editing hyper-mutation local convergence clonal selection algorithm |
