Abstract
Prediction involves estimating the unknown value of an attribute of a system under study given the values of other measured attributes. In prediction (machine) learning the prediction rule is derived from data consisting of previously solved cases. Most methods for predictive learning were originated many years ago at the dawn of the computer age. Recently two new techniques have emerged that have revitalized the field. These are support vector machines and boosted decision trees. This paper provides an introduction to these two new methods tracing their respective ancestral roots to standard kernel methods and ordinary decision trees.
Similar content being viewed by others
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Friedman, J. Recent Advances in Predictive (Machine) Learning. Journal of Classification 23, 175–197 (2006). https://doi.org/10.1007/s00357-006-0012-4
Issue Date:
DOI: https://doi.org/10.1007/s00357-006-0012-4