Numerical analysis of least squares and perceptron learning for classification problems

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.