Advances in Boosting (Invited Talk)

TL;DR

This paper reviews the AdaBoost boosting algorithm, its theoretical foundations, and demonstrates its application in auction price prediction and spoken-dialogue classification, addressing domain-specific challenges.

Contribution

It provides an overview of AdaBoost's theory and illustrates its practical use in complex, real-world problems involving uncertainty and large unlabeled data streams.

Findings

Boosting improves prediction accuracy in auction price modeling.

Incorporating prior knowledge enhances classification in dialogue systems.

Selective sampling of unlabeled data improves learning efficiency.

Abstract

Boosting is a general method of generating many simple classification rules and combining them into a single, highly accurate rule. In this talk, I will review the AdaBoost boosting algorithm and some of its underlying theory, and then look at how this theory has helped us to face some of the challenges of applying AdaBoost in two domains: In the first of these, we used boosting for predicting and modeling the uncertainty of prices in complicated, interacting auctions. The second application was to the classification of caller utterances in a telephone spoken-dialogue system where we faced two challenges: the need to incorporate prior knowledge to compensate for initially insufficient data; and a later need to filter the large stream of unlabeled examples being collected to select the ones whose labels are likely to be most informative.