Effective transductive learning via objective model selection

✍ Scribed by Ran El-Yaniv; Leonid Gerzon

• Publisher: Elsevier Science
• Year: 2005
• Tongue: English
• Weight: 439 KB
• Volume: 26
• Category: Article
• ISSN: 0167-8655
• DOI: 10.1016/j.patrec.2005.03.025

✦ Synopsis

This paper is concerned with transductive learning. We study a recent transductive learning approach based on clustering. In this approach one constructs a diversity of unsupervised models of the unlabeled data using clustering algorithms. These models are then exploited to construct a number of hypotheses using the labeled data and the learner selects an hypothesis that minimizes a transductive error bound, which holds with high probability. Empirical examination of this approach, implemented with Ôspectral clusteringÕ, on a suite of benchmark datasets from the UCI repository, indicates that the new approach is effective and comparable with one of the best known transductive learning algorithms to-date.