What Is Pattern Recognition?

Pattern recognition is the art of finding patterns in data. It's a bit like a treasure hunt, except there's no treasure, and instead of a map, you've got some data to work with. It's a branch of machine learning that emphasizes the recognition of data patterns or data regularities in a given scenario. It can be either "supervised," where previously known patterns can be found in a given data, or "unsupervised," where entirely new patterns are discovered. Pattern recognition is different from machine learning. It's one of its subfields but only covers some of what machine learning does. Pattern recognition algorithms are a way to classify input data into objects or classes based on specific features. In other words, they're like a detective who tries to figure out what happened at a crime scene by looking for clues and then putting them together. They can be used in all sorts of different ways, from recognizing patterns in your shoe size to identifying whether there's an animal in your picture. Pattern recognition and pattern matching often need clarification, but they differ. Pattern recognition is looking for a similar pattern in a given data set. It relies on intuition and common sense to determine which pattern best fits your data. For example, if you're trying to identify a picture of a dog, look for the most likely patterns in the image. Maybe it has four legs, or it's brown and white with floppy ears, and it uses those as clues that it's probably a dog. On the other hand, pattern matching relies on an exact match of an already-known pattern with your data set. The process is transparent and straightforward: you look for exactly what you know to be there (i.e., a perfect circle), not just something that looks like it might be there (i.e., an oval).