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Activity Number: 98
Type: Topic Contributed
Date/Time: Monday, July 30, 2007 : 8:30 AM to 10:20 AM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #308985
Title: An Iterative Algorithm for Extending Learners to a Semisupervised Setting
Author(s): Mark Culp*+ and George Michailidis
Companies: University of Michigan and The University of Michigan
Address: 1843 Pointe Crossing 201, Ann Arbor, MI, 48105,
Keywords: Machine learning ; Non-parametric smoothing ; Additive Models ; CART ; Semi-Supervised
Abstract:

In this talk, we present an iterative algorithm, whose objective is to extend learners from a supervised setting into a semi-supervised setting. The algorithm is based on using the predicted response values for observations where it is missing (unlabeled data) and then incorporates the predictions appropriately at subsequent stages. Convergence properties of the algorithm are investigated for particular learners, such as linear/logistic regression, kernel smoothers, generalized additive models, tree partitioning methods, partial least squares, etc. The algorithm is illustrated on a number of real data sets using a varying degree of labeled response.

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Revised September, 2007