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Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers

Cozman Fabio and Cohen Ira

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0004
Subject:
Computer Science, Machine Learning

This chapter presents a number of conclusions. Firstly, labeled and unlabeled data contribute to a reduction in variance in semi-supervised learning under maximum-likelihood estimation. Secondly, ... More


An Augmented PAC Model for Semi-Supervised Learning

Balcan Maria-Florina and Blum Avrim

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0022
Subject:
Computer Science, Machine Learning

This chapter describes an augmented version of the PAC model, designed with semi-supervised learning in mind, that can be used to help think about the problem of learning from labeled and unlabeled ... More


Entropy Regularization

Grandvalet Yves and Bengio Yoshua

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0009
Subject:
Computer Science, Machine Learning

This chapter promotes the use of entropy regularization as a means to benefit from unlabeled data in the framework of maximum a posteriori estimation. The learning criterion is derived from clearly ... More


Semi-Supervised Protein Classification Using Cluster Kernels

Weston Jason, Leslie Christina, Ie Eugene, and Noble William Stafford

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0019
Subject:
Computer Science, Machine Learning

This chapter describes an experimental study of large-scale semi-supervised learning for the problem of protein classification. The protein classification problem, a central problem in computational ... More


Introduction to Semi-Supervised Learning

Chapelle Olivier, Schölkopf Bernhard, and Zien Alexander

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0001
Subject:
Computer Science, Machine Learning

This chapter first presents definitions of supervised and unsupervised learning in order to understand the nature of semi-supervised learning (SSL). SSL is halfway between supervised and unsupervised ... More


Gaussian Processes and the Null-Category Noise Model

Lawrence Neil D. and Jordan Michael I.

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0008
Subject:
Computer Science, Machine Learning

This chapter presents an augmentation of the standard probabilistic classification model which incorporates a null-category. Given a suitable probabilistic model for the model category, the chapter ... More


Data-Dependent Regularization

Corduneanu Adrian and Jaakkola Tommi

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0010
Subject:
Computer Science, Machine Learning

This chapter considers two ways of representing the topology over examples, either based on complete knowledge of the marginal density or by grouping together examples whose labels should be related. ... More


Large-Scale Algorithms

Delalleau Olivier, Bengio Yoshua, and Le Roux Nicolas

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0018
Subject:
Computer Science, Machine Learning

This chapter presents a subset selection method that can be used to reduce the original system to one of size m 〈〈 n. The idea is to solve for the labels of a subset S ⊂ X of only m points, while ... More


Transductive Inference and Semi-Supervised Learning

Vapnik Vladimir

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0024
Subject:
Computer Science, Machine Learning

This chapter discusses the difference between transductive inference and semi-supervised learning. It argues that transductive inference captures the intrinsic properties of the mechanism for ... More


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