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Erschienen in:

2018 | OriginalPaper | Buchkapitel

11. Semi-supervised Smoothing for Large Data Problems

verfasst von : Mark Vere Culp, Kenneth Joseph Ryan, George Michailidis

Erschienen in: Handbook of Big Data Analytics

Verlag: Springer International Publishing

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This book chapter is a description of some recent developments in non-parametric semi-supervised regression and is intended for someone with a background in statistics, computer science, or data sciences who is familiar with local kernel smoothing (Hastie et al., The elements of statistical learning (data mining, inference and prediction), chapter 6. Springer, Berlin, 2009). In many applications, response data often require substantially more effort to obtain than feature data. Semi-supervised learning approaches are designed to explicitly train a classifier or regressor using all the available responses and the full feature data. This presentation is focused on local kernel regression methods in semi-supervised learning and provides a good starting point for understanding semi-supervised methods in general.

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Vorheriges Kapitel Analysis of High-Dimensional Regression Models Using Orthogonal Greedy Algorithms

Nächstes Kapitel Inverse Modeling: A Strategy to Cope with Non-linearity

Note, one could weight the loss function as \(\left (\boldsymbol {Y}_L-{\boldsymbol {f}}_L\right )^T{\boldsymbol {W}}_{LL}\left (\boldsymbol {Y}_L-{\boldsymbol {f}}_L\right )\), however, to our knowledge this specific loss function in the context of semi-supervised learning with a labeled loss approach has not been studied.

Abney S (2004) Understanding the Yarowsky algorithm. Comput Linguist 30(3):365–395MathSciNetCrossRef

Abney S (2008) Semisupervised learning for computational linguistics. Chapman and Hall, CRC, Boca Raton

Belkin M, Matveeva I, Niyogi P (2004) Regularization and semi-supervised learning on large graphs. In: COLT, pp 624–638

Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399–2434

Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Computational learning theory, pp 92–100

Bredel M, Jacoby E (2004) Chemogenomics: an emerging strategy for rapid target and drug discovery. Nat Rev Genet 5(4):262–275CrossRef

Chapelle O, Schölkopf B, Zien A (2006) Semi-supervised learning. MIT Press, Cambridge. http://www.kyb.tuebingen.mpg.de/ssl-book

Chapelle O, Sindhwani V, Keerthi S (2008) Optimization techniques for semi-supervised support vector machines. J Mach Learn Res 9:203–233

Culp M, Michailidis G (2008) An iterative algorithm for extending learners to a semi-supervised setting. J Comput Graph Stat 17(3):545–571MathSciNetCrossRef

Culp M, Ryan K (2013) Joint harmonic functions and their supervised connections. J Mach Learn Res 14:3721–3752

Gong C, Liu T, Tao D, Fu K, Tu E, Yang J (2015) Deformed graph Laplacian for semisupervised learning. IEEE Trans Neural Nets Learn Syst 26:2261–2274MathSciNetCrossRef

Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning (data mining, inference and prediction). Springer, Berlin

Jebara T, Wang J, Chang S (2009) Graph construction and b-matching for semi-supervised learning. In: International conference of machine learning

Koprinska I, Poon J, Clark J, Chan J (2007) Learning to classify e-mail. Inf Sci 177(10):2167–2187CrossRef

Lafferty J, Wasserman L (2007) Statistical analysis of semi-supervised regression. In: Advances in NIPS. MIT Press, Cambridge, pp 801–808

Liu W, He J, Chang S (2010) Large graph construction for scalable semi-supervised learning. In: International conference of machine learning

Lundblad R (2004) Chemical reagents for protein modification. CRC Press, Boca Raton

McCallum A, Nigam K, Rennie J, Seymore K (2000) Automating the construction of internet portals with machine learning. Inf Retr J 3:127–163

Shilang S (2013) A survey of multi-view machine learning. Neural Comput Appl 7–8(28):2013–2038

Wang J, Shen X (2007) Large margin semi-supervised learning. J Mach Learn Res 8:1867–1897

Wang J, Jebara T, Chang S (2013) Semi-supervised learning using greedy max-cut. J Mach Learn Res 14:771–800

Yamanishi Y, Vert J, Kanehisa M (2004) Protein network inference from multiple genomic data: a supervised approach. Bioinformatics 20:363–370CrossRef

Zhou D, Bousquet O, Lal TN, Weston J, Schölkopf B (2004) Learning with local and global consistency. In: Advances in neural information processing systems 16

Zhu X (2008) Semi-supervised learning literature survey. Technical report, Computer Sciences, University of Wisconsin-Madison

Titel: Semi-supervised Smoothing for Large Data Problems
verfasst von: Mark Vere Culp
Kenneth Joseph Ryan
George Michailidis
Verlag: Springer International Publishing
Buch: Handbook of Big Data Analytics
Print ISBN: 978-3-319-18283-4

Electronic ISBN: 978-3-319-18284-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-18284-1_11

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