Instance-based learning

Instance-based learning is a type of machine learning where a computer looks at examples and tries to recognize patterns between them. It works by looking at groups of examples, called "instances," and then using those instances to figure out how to solve a problem. For example, say you want to teach a computer to recognize cats. You might give it a lot of pictures of cats in different positions, with different colors and patterns, and with different backgrounds. The computer would look at all of these examples and try to find similarities between them. Once it does, it can then recognize a cat in a new picture even if it hasn't seen that exact cat before!