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Supervised learning

Known as: Fully-supervised machine learning, Supervised Machine Learning, Supervised classification 
Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training… 
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Papers overview

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Highly Cited
2020
Highly Cited
2020
Cross entropy is the most widely used loss function for supervised training of image classification models. In this paper, we… 
Highly Cited
2016
Highly Cited
2016
We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to… 
Highly Cited
2010
Highly Cited
2010
  • Xiaojin Zhu
  • Encyclopedia of Machine Learning
  • 2010
  • Corpus ID: 3869071
Semi-supervised learning uses both labeled and unlabeled data to perform an otherwise supervised learning or unsupervised… 
Highly Cited
2009
Highly Cited
2009
Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans… 
Highly Cited
2008
Highly Cited
2008
It is now well established that sparse signal models are well suited for restoration tasks and can be effectively learned from… 
Review
2006
Review
2006
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in… 
Highly Cited
2006
Highly Cited
2006
A number of supervised learning methods have been introduced in the last decade. Unfortunately, the last comprehensive empirical… 
Highly Cited
2006
Highly Cited
2006
Social network analysis has attracted much attention in recent years. Link prediction is a key research directions within this… 
Highly Cited
2004
Highly Cited
2004
We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this… 
Highly Cited
2003
Highly Cited
2003
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data…