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Semi-Supervised Learning$
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Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien

Print publication date: 2006

Print ISBN-13: 9780262033589

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262033589.001.0001

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PRINTED FROM MIT PRESS SCHOLARSHIP ONLINE (www.mitpress.universitypressscholarship.com). (c) Copyright The MIT Press, 2022. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 01 July 2022

Analysis of Benchmarks

Analysis of Benchmarks

(p.376) (p.377) 25 Analysis of Benchmarks
Semi-Supervised Learning

Chapelle Olivier

Schölkopf Bernhard

Zien Alexander

The MIT Press

This chapter assesses the strengths and weaknesses of different semi-supervised learning (SSL) algorithms through inviting the authors of each chapter in this book to apply their algorithms to eight benchmark data sets. These data sets encompass both artificial and real-world problems. Details are provided on how the algorithms were applied, especially how hyperparameters were chosen given the few labeled points. Finally, the chapter concludes by presenting and discussing the empirical performance.

Keywords:   semi-supervised learning algorithms, SSL, benchmark data sets, real-world problems, hyperparameters, empirical performance

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