This work presents study on regularized and non-regularized versions of perceptron learning and least squares algorithms for classification problems. Fr'echet derivatives for regularized least squares and perceptron learning algorithms are derived. Different Tikhonov's regularization techniques for choosing the regularization parameter are discussed. Decision boundaries obtained by non-regularized algorithms to classify simulated and experimental data sets are analyzed.

中文翻译：

---

分类问题的最小二乘和感知器学习的数值分析

这项工作介绍了对正则化和非正则化版本的感知器学习和分类问题的最小二乘算法的研究。推导出正则化最小二乘法和感知器学习算法的 Fr'echet 导数。讨论了用于选择正则化参数的不同 Tikhonov 正则化技术。分析了通过非正则化算法对模拟和实验数据集进行分类而获得的决策边界。