Analysis of Benchmarks

Chapelle, O; Schölkopf, B; Zien, A

doi:10.7551/mitpress/9780262033589.003.0021

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Analysis of Benchmarks

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Chapelle, O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zien, A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://www.molgen.mpg.de/3659531/MITPress--SemiSupervised-Learning.pdf
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引用

Chapelle, O., Schölkopf, B., & Zien, A. (2006). Analysis of Benchmarks. In O., Chapelle, B., Schölkopf, & A., Zien (Eds.), Semi-Supervised Learning (pp. 377-393). Cambridge, MA, USA: MIT Press.

引用: https://hdl.handle.net/21.11116/0000-0004-9CF7-6

要旨

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.