Gaussian Process Regression From First Principles

 By Medium - 2021-03-15

Description

Gaussian Process Regression (GPR) is a remarkably powerful class of machine learning algorithms that, in contrast to many of today’s state-of-the-art machine learning models, relies on few parameters…

Summary

  • Gaussian Process Regression is a remarkably powerful class of machine learning algorithms.
  • Estimates of the mean of f(x) are produced as a linear combination of observed target values y.
  • The weighting coefficients used to produce these mean estimates are independent of the target values, placing Gaussian Process Regression models into the class of linear smoothers [1].
  • Photo by Siora Photography on Unsplash Covariance functions are a crucial component of GPR models, sincethese functions weight the contributions of training points to predicted test targets according to the kernel distance between observed training points X and test points X∗.
  • Recall from the previous section that one way to conceptualize GPR prediction is as a linear smoothing mechanism: An example of time series mean and variance prediction using Gaussian Process Regression.

 

Topics

  1. Machine_Learning (0.5)
  2. Backend (0.19)
  3. NLP (0.16)

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