Kernel methods

Kernel methods are mathematical techniques used in machine learning that allow algorithms to identify complex patterns in data without explicitly transforming it into higher dimensions. They work by computing a similarity measure—called a kernel—between data points directly in their original space. This approach enables models, such as support vector machines, to efficiently classify or analyze data that isn't linearly separable, effectively capturing intricate relationships. Think of kernels as a way to compare data points in a more flexible and powerful way, making it easier to find meaningful patterns without heavy computation.