Introduction: The Abstract Forest Model

Abstract

Problems related to the automatic or semi-automatic analysis of complex data such as photographs, videos, medical scans, text or genomic data can all be categorized into a relatively small set of prototypical machine learning tasks. The popularity of decision forests is mostly due to their recent success in classification tasks. However, forests are a more general tool which can be applied to many additional problems. This chapter presents a unified model of decision forests which can be used to tackle all the common learning tasks: classification, regression, density estimation, manifold learning, semi-supervised learning, and active learning.