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Computational Models of Language Universals: Expressiveness, Learnability, and Consequences

Edward P. Stabler

in Language Universals

Published in print:
2009
Published Online:
May 2009
ISBN:
9780195305432
eISBN:
9780199866953
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780195305432.003.0010
Subject:
Psychology, Cognitive Psychology, Cognitive Models and Architectures

This chapter reports on research showing that it may be a universal structural property of human languages that they fall into a class of languages defined by mildly context-sensitive grammars. It ... 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


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


Transductive Support Vector Machines

Joachims Thorsten

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/9780262033589.003.0006
Subject:
Computer Science, Machine Learning

This chapter discusses the transductive learning setting proposed by Vapnik where predictions are made only at a fixed number of known test points. Transductive support vector machines (TSVMs) ... More


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