• Contents
• Introduction
  • The Vizier 1.0 User Interface
  • The data opportunity
• Simple Solutions
  • Linear regression
  • Nearest neighbor
• Memory Based Learning
  • A distance metric
  • Near neighbors
  • Weighting function
  • How to fit the local points
  • Multivariate Learning
  • Classification
• Using Locally Weighted Learning for Modeling
  • ``Hands-off'' non-parametric relation finding
  • Low dimensional supervised learning
  • Complex function of a subset of inputs
  • Simple function of most inputs, but complex function of a few
  • Complex function of a few features of many inputs
  • Pros and cons vs. neural networks
  • When should locally weighted learning be used?
• The Information Provided by a Learned Local Model
  • Prediction distributions
  • Noise estimate distributions
  • Gradient estimate distributions
  • Other things provided by local weighted models
  • Why do we want all these estimates?
• Bayesian Locally Weighted Regression
• Efficient Data Storage and Retrieval
• Autonomous Modeling
  • Judging Model Quality by Residuals
  • Cross Validation
  • Blackbox Model Selection
• Decisions with Locally Weighted Models
  • Choosing Parameters to Achieve a Target
  • Maximizing or Minimizing a Learned Model
  • Using Locally Weighted Learning to Design Experiments
  • Programming with the Vizier library
• References
• About this document ...