Pattern recognition is the ability to identify a pattern or a regularity in a set of elements. In computer science, pattern recognition is an artificial intelligence technique concerned with the development of methods to detect and classify regularities in data such as images, sounds, or texts. Patterns can be discovered based on statistical analysis and historical data.The pattern recognition method is characterized by the following features:
High speed and accuracy for familiar pattern detection.
The ability to recognize unknown elements.
The capacity to distinguish diverse attributes and objects from the environment.
The ability to discover patterns and objects that are partially hidden.
Pattern recognition can be divided into three main categories:Supervised pattern recognitionIt is a method in which a computer algorithm is trained by a human to recognize patterns based on a predefined set of labeled data and then classify new data.Unsupervised pattern recognitionIn this case, the model learns without direct guidance. The algorithm finds correlations between different data elements (inputs) based on their similarity.Unsupervised learning can be used together with supervised learning to improve the quality of results or used separately.Reinforcement learningIn reinforcement learning, the agent finds a solution to the problem by trial and error. The AI is provided with an environment and learns how to work in that environment to reap the biggest rewards.For a detailed explanation of how pattern recognition works, read our post about the basics of pattern recognition and ML.