Artificial Intelligence and Neural Networks

Self-Organizing Neural Networks

The study of patterns and regularities in data is a fundamental aspect of machine learning, with pattern recognition being a prominent application. This involves using supervised learning algorithms to develop classifiers trained on data from various object categories. Supervised pattern recognition enables applications such as optical character recognition (OCR), face detection, face recognition, object detection, and object classification. In contrast, unsupervised learning identifies hidden structures within data using clustering techniques.

Feature selection, also known as variable selection, is the process of identifying a subset of relevant features for model development. It helps reduce overfitting, shorten training times, and simplify models for better interpretability by eliminating unnecessary or redundant features with minimal or no loss of information.

Key phases in pattern recognition include:

• Learning Phase
• Prediction Phase