Third edition of Artificial Intelligence: foundations of computational agents, Cambridge University Press, 2023 is now available (including the full text).
7.3 Basic Models for Supervised Learning
A learned model is a representation of a function from the input features to the target features. Most supervised learning methods take the input features, the target features, and the training data and return a model that can be used for future prediction. Many of the learning methods differ in what representations are considered for representing the function. We first consider some basic models from which other composite models are built. Section 7.4 considers more sophisticated models that are built from these basic models.
