Gaussian Processes: Covariance Functions (Kernels)
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Adapted from an appendix of my MS thesis. Covariance Functions A covariance function encodes our assumptions about the function which we wish to learn. In supervised learning, the notion of similarity between data points is also crucial. It is a basic assumption that points x p and x q that are close are likely to have a similar target value y . Under the Gaussian process view it is the covariance function that defines nearness or similarity [1]. A stationary covariance function is a…
1Key Takeaways
- Adapted from an appendix of my MS thesis.
- Covariance Functions A covariance function encodes our assumptions about the function which we wish to learn.
- In supervised learning, the notion of similarity between data points is also crucial.
- It is a basic assumption that points x p and x q that are close are likely to have a similar target value y .
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that adapted from an appendix of my MS thesis.
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