Overview and Scope

Abstract

This book presents a unified, efficient model of decision forests which can be used in a number of applications such as scene recognition from photographs, object recognition in images and automatic diagnosis from radiological scans. Such applications have traditionally been addressed by different, supervised or unsupervised machine learning techniques. However, in this book, diverse learning tasks, including regression, classification, and semi-supervised learning are all seen as instances of the same general decision forest model. The unified framework further extends to novel uses of forests in tasks such as density estimation and manifold learning. This unification carries both theoretical and practical advantages. For instance, the underlying single model gives us the opportunity to implement and optimize the general algorithm for all these tasks only once, and then easily adapt it to individual applications with relatively small changes.