NDLI: AdaTree: Boosting a Weak Classifier into a Decision Tree

Content Provider	IEEE Xplore Digital Library
Author	Grossmann, E.
Copyright Year	2004
Description	Author affiliation: University of Kentucky, Lexington (Grossmann, E.)
Abstract	We present a boosting method that results in a decision tree rather than a fixed linear sequence of classifiers. An equally correct statement is that we present a tree-growing method whose performance can be analysed in the framework of Adaboost. We argue that Adaboost can be improved by presenting the input to a sequence of weak classifiers, each one tuned to the conditional probability determined by the output of previous weak classifiers. As a result, the final classifier has a tree structure, rather than being linear, thus the name "Adatree". One of the consequences of the tree structure is that different input data may have different processing time. Early experimentation shows a reduced computation cost with respect to Adaboost. One of our intended applications is real-time detection, where cascades of boosted detectors have recently become successful. The reduced computation cost of the proposed method shows some potential for being used directly in detection problems, without need of a cascade.
Starting Page	105
Ending Page	105
File Size	551358
Page Count	1
File Format	PDF
DOI	10.1109/CVPR.2004.296
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Conferences Boosting Pattern recognition Decision trees Computer Society Classification tree analysis
Content Type	Text
Resource Type	Article

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