论文标题
对分类问题的最小二乘和感知性学习的数值分析
Numerical analysis of least squares and perceptron learning for classification problems
论文作者
论文摘要
这项工作介绍了有关分类问题的知名度和最小二乘算法的正规化和非规范化版本的研究。得出了正规化最小二乘和感知算法的FR'Echet衍生物。讨论了不同的Tikhonov选择正规化参数的正规化技术。通过非规范化算法获得的决策边界分析了模拟和实验数据集。
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.
