MoL-2005-02: The Value of Agreement: a new Boosting Algorithm

MoL-2005-02: Leskes, Boaz (2005) The Value of Agreement: a new Boosting Algorithm. [Report]

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In the past few years unlabeled examples and their potential advantage have received a lot of attention. In this paper a new boosting algorithm is presented where unlabeled examples are used to enforce agreement between several different learning algorithms. Not only do the learning algorithms learn from the given training set but they are supposed to do so while agreeing on the unlabeled examples. Similar ideas have been proposed before (for example, the Co-Training algorithm by Mitchel and Blum), but without a proof or under strong assumptions. In our setting, it is only assumed that all learning algorithms are equally adequate for the tasks. A new generalization bound is presented where the use of unlabeled examples results in a better ratio between training-set size and the the resulting classifier's quality. The extent of this improvement depends on the diversity of the learners--a more diverse group of learners will result in a larger improvement whereas using two copies of a single algorithm gives no advantage at all. As a proof of concept, the algorithm, named AgreementBoost, is applied to two test problems. In both cases, using AgreementBoost results in an up to 40% reduction in the number of labeled examples.

Item Type: Report
Report Nr: MoL-2005-02
Series Name: Master of Logic Thesis (MoL) Series
Year: 2005
Uncontrolled Keywords: automated learning
Date Deposited: 12 Oct 2016 14:38
Last Modified: 12 Oct 2016 14:38

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