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Semi-Supervised Learning

Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien (eds)

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
book
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.001.0001
Subject:
Computer Science, Machine Learning

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in ... 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


A Discussion of Semi-Supervised Learning and Transduction

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/9780262033589.003.0025
Subject:
Computer Science, Machine Learning

This chapter presents a fictitious discussion inspired by real discussions between the editors of this book and a number of people, including Vladimir Vapnik. It involves three researchers; for ... More


Label Propagation and Quadratic Criterion

Bengio Yoshua, Delalleau Olivier, and Roux Nicolas Le

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/9780262033589.003.0011
Subject:
Computer Science, Machine Learning

This chapter shows how the different graph-based algorithms for semi-supervised learning can be cast into a common framework where one minimizes a quadratic cost criterion whose closed-form solution ... More


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


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


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


Metric-Based Approaches for Semi-Supervised Regression and Classification

Schuurmans Dale, Southey Finnegan, Wilkinson Dana, and Guo Yuhong

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/9780262033589.003.0023
Subject:
Computer Science, Machine Learning

This chapter discusses the explicit relationship that must be asserted between labeled and unlabeled data, which is a requirement of semi-supervised learning methods. Semi-supervised model selection ... More


Modifying Distances

Orlitsky Alon

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/9780262033589.003.0017
Subject:
Computer Science, Machine Learning

This chapter discusses density-based metrics induced by Riemannian manifold structures. It presents asymptotically consistent methods to estimate and compute these metrics and present upper and lower ... More


Semi-Supervised Text Classification Using EM

Nigam Kamal, McCallum Andrew, and Mitchell Tom

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/9780262033589.003.0003
Subject:
Computer Science, Machine Learning

This chapter explores the use of generative models for semi-supervised learning with labeled and unlabeled data in domains of text classification. The widely used naive Bayes classifier for ... More


Analysis of Benchmarks

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/9780262033589.003.0021
Subject:
Computer Science, Machine Learning

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 ... 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


Prediction of Protein Function from Networks

Shin Hyunjung and Tsuda Koji

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/9780262033589.003.0020
Subject:
Computer Science, Machine Learning

This chapter describes an algorithm to assign weights to multiple graphs within graph-based semi-supervised learning. Both predicting class labels and searching for weights for combining multiple ... More


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