DeepAI AI Chat
Log In Sign Up

Advances in Boosting (Invited Talk)

by   Robert E. Schapire, et al.

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.


page 1

page 3

page 4

page 6

page 7


Re-scale boosting for regression and classification

Boosting is a learning scheme that combines weak prediction rules to pro...

Distorted English Alphabet Identification : An application of Difference Boosting Algorithm

The difference-boosting algorithm is used on letters dataset from the UC...

Recursive Bias Estimation and L_2 Boosting

This paper presents a general iterative bias correction procedure for re...

Better Short than Greedy: Interpretable Models through Optimal Rule Boosting

Rule ensembles are designed to provide a useful trade-off between predic...

Being Properly Improper

In today's ML, data can be twisted (changed) in various ways, either for...

Boosting in Location Space

The goal of object detection is to find objects in an image. An object d...

The boosted HP filter is more general than you might think

The global financial crisis and Covid recession have renewed discussion ...