Comparative Analysis of Supervised and Unsupervised Learning Methods for Pattern Classification

Abstract

In the higher learning system, this article compares and contrasts supervised and unsupervised learning approaches to see which is more effective for classifying patterns. Among the most significant uses of machine learning algorithms is classification. Our research shows that, although the supervised learning algorithm, Backpropagation learning with errors, does a great deal of nonlinear real-time assignments, the unsupervised learning algorithm, Kohonen Self-Organizing Map (KSOM), performs very well in our study's classification tasks.