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Semi-supervised learning

Known as: SSL, Semi supervised learning, Semisupervised learning 
Semi-supervised learning is a class of supervised learning tasks and techniques that also make use of unlabeled data for training – typically a small… Expand
Wikipedia

Related topics

Related topics

42 relations
Active learning (machine learning)Classic monolingual word-sense disambiguationCo-trainingConcept learning
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Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2017
Highly Cited
2017
Semi-Supervised Classification with Graph Convolutional Networks
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of… Expand
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Highly Cited
2015
Highly Cited
2015
Semi-supervised Learning with Ladder Networks
We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to… Expand
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Highly Cited
2014
Highly Cited
2014
Semi-supervised Learning with Deep Generative Models
The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised… Expand
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Highly Cited
2010
Highly Cited
2010
Word Representations: A Simple and General Method for Semi-Supervised Learning
If we take an existing supervised NLP system, a simple and general way to improve accuracy is to use unsupervised word… Expand
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Highly Cited
2009
Highly Cited
2009
Introduction to Semi-Supervised Learning
Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans… Expand
Review
2006
Review
2006
Semi-Supervised Learning
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in… Expand
Highly Cited
2006
Highly Cited
2006
Introduction to Semi-Supervised Learning
This chapter contains sections titled: Supervised, Unsupervised, and Semi-Supervised Learning, When Can Semi-Supervised Learning… Expand
Review
2005
Review
2005
Semi-supervised learning with graphs
In traditional machine learning approaches to classification, one uses only a labeled set to train the classifier. Labeled… Expand
Highly Cited
2004
Highly Cited
2004
Semi-supervised Learning by Entropy Minimization
We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this… Expand
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Highly Cited
2003
Highly Cited
2003
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data… Expand
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