This paper reports on a study to model seabed surfaces using the least-squares support-vector machine algorithm with a sample cross-validation (CV) method. It starts with a brief overview of the sample selection method of the algorithm and gives two important characteristics of the algorithm. It then focuses on the theory of sample CV and the steps of sample selection using this theory. Finally, to verify the validity of the sample CV method in sample selection for the algorithm, the measured multi-beam bathymetric data are selected to calculate and analyse. It is concluded that the sample CV method can reasonably screen out the sounding training samples with a large contribution to the function model, making the constructed function model more reasonable.

中文翻译：

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使用最小二乘支持向量机进行海床建模和样本交叉验证

本文报道了使用最小二乘支持向量机算法和样本交叉验证（CV）方法对海床表面建模的研究。首先简要概述了该算法的样本选择方法，并给出了该算法的两个重要特征。然后重点介绍样本CV的理论以及使用该理论的样本选择步骤。最后，为了验证该算法在样本选择中的有效性，选择了测得的多光束测深数据进行计算和分析。结论：样本CV方法可以合理地筛选出对功能模型有很大贡献的测深训练样本，使构建的功能模型更加合理。