A visual introduction to kernel functions
a day ago
- #Gaussian Processes
- #Machine Learning Modeling
- #Kernel Functions
- A machine learning model is an approximation of an unknown relationship, such as mapping cheese (C) to gold (G), based on observed data.
- Gaussian processes (GPs) represent a distribution over functions, providing mean predictions and uncertainty estimates using a mean function and a covariance (kernel) function.
- Kernel functions measure similarity between data points and influence the GP's shape; examples include linear, periodic, RBF, rational quadratic, and Matérn kernels.
- Kernels can be added or multiplied to form composites, allowing models to capture complex patterns like combined linear and periodic trends.
- The choice of kernel introduces inductive bias, tailoring the GP to specific data characteristics, with parameters like length scales controlling smoothness and sensitivity.