计算机科学 ›› 2020, Vol. 47 ›› Issue (12): 262-266.doi: 10.11896/jsjkx.200500085
王红星1, 陈玉权1, 沈杰1, 张欣1, 黄祥1, 于滨2
WANG Hong-xing1, CHEN Yu-quan1, SHEN Jie1, ZHANG Xin1, HUANG Xiang1, YU Bin2
摘要： 基于机器学习的视觉探伤技术已经被广泛地应用于包括锈蚀检测在内的工业领域.针对已有算法存在的复杂度高、依赖大量人工标注等问题文中提出了一种新型半监督极限学习机HyLap-S3ELM用于防震锤锈蚀缺陷检测.其具有以下优点:模型参数存在封闭解因此可以直接计算得到对运算资源的依赖性较小;引入了超图拉普拉斯矩阵可以更好地描述数据的平滑性以提升半监督分类的精度;引入了风险正则化项当数据平滑性假设不准确或者有标注样本存在偏移时能够提升半监督分类器的稳定性.最后通过大量实验证明了所提方法的有效性与优越性.
中图分类号:
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