Artificial Intelligence and Neural Networks

Self-Organizing Neural Networks

The study of patterns and regularities in knowledge is a key component of machine learning, and pattern recognition is one such application. This is done by utilising supervised learning algorithms to create classifiers that are backed by training data from a variety of object categories. With the use of supervised pattern recognition, optical character recognition (OCR) can detect faces, identify faces, identify objects, and classify objects. Unsupervised learning consequently operates by identifying hidden structures using clustering techniques.

The process of choosing a subset of pertinent features to be used in model creation is known as feature selection or variable selection. Additionally, they are utilised to reduce overfitting, shorten training times, and simplify the models to make them easier to understand (reduction of variables). Numerous characteristics in the data are unnecessary or redundant, and they can be deleted with little to no information loss.