Gaussian process classification
(30 minutes to learn)
Gaussian process classification is a Bayesian model for nonparametric classification. The data points have associated latent variables which are drawn from a GP prior, and the labels are modeled as stochastic functions of the latent variables.
This concept has the prerequisites:
Core resources (read/watch one of the following)
→ Gaussian Processes for Machine Learning
A graduate-level machine learning textbook focusing on Gaussian processes.
Location: Section 3.3, pages 39-41
→ Pattern Recognition and Machine Learning
A textbook for a graduate machine learning course, with a focus on Bayesian methods.
Location: Section 6.4.5, pages 313-315
- We can approximate the solution to GP classification using the Laplace approximation .
- Gaussian processes have a variety of uses in machine learning, including:
- black-box optimization (where we only get to evaluate the function, and doing so is expensive)
- reinforcement learning
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