Learning from Labeled and Unlabeled Data

By Geoffrey I. Webb, Claude Sammut, Claudia Perlich, Tamás Horváth, Stefan Wrobel, Kevin B. Korb, W. S. Noble, Christina Leslie, Michail G. Lagoudakis, Novi Quadrianto and 8 more. Encyclopedia of Machine Learning, 2010

355 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/c7ff12c05795232dd971b0e5bc849e51a1277899

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In Artificial Intelligence

Citations in the corpus

Introduction to Semi-Supervised Learning (2009, background): “For further readings on these and other semi-supervised learning topics, there is a book collection from a machine learning perspective [37], a survey article with up-to-date papers [208], a book written for computational linguists [1], and a technical report [151].”
Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation (2015, neutral): “ata is readily available and can be leveraged by SSL approaches to improve the performance of supervised prediction systems. There are several surveys that cover various SSL methods in the literature [25, 37, 8, 6]. The majority of SSL algorithms are computationally expensive; for example, transduct”
A Lazy Man's Approach to Benchmarking: Semisupervised Classifier Evaluation and Recalibration (2013, methodology): “This setting is similar to semisupervised learning [20, 21].”
Semi-supervised Learning in Causal and Anticausal Settings (2013, background): “Finally, note that the issue is also related to the distinction between generative and discriminative learning; see, for instance, [15].”
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning (2016, result): “Our results also might seem to contradict the result by Sokolovska et al. (2008) (and, by extension Kawakita & Takeuchi (2014)) that, when the supervised model is misspecified, a particular semisupervised adaptation of logistic regression has an asymptotic variance that is at least as small as super”
Zero-Shot Object Detection (2018, background): “This is reminiscent of semi-supervised learning algorithms [48]; we have annotated objects for seen classes and unlabeled boxes for the rest of the image regions.”
Semi-Supervised Learning (2019, neutral): “Many semi-supervised learning methods have been proposed, with different answers to these two questions [15, 7, 1, 10].”
Transfer learning for image classification with sparse prototype representations (2008, background): “, [17]), but don’t generally exploit knowledge learned from previous supervised tasks.”
Semi-Supervised Learning (2014, neutral): “Many semi-supervised learning methods have been proposed, with different answers to these two questions [15, 7, 1, 10].”
Mean Map Kernel Methods for Semisupervised Cloud Classification (2010, result): “This argument is akin to that in [29]–[33] and often is called the cluster assumption [31], [32].”