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Co-training

Co-training is a machine learning algorithm used when there are only small amounts of labeled data and large amounts of unlabeled data. One of its… 
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Related topics

Related topics

9 relations
CoBoostingComputer scienceCoupled pattern learnerFunctional genomics

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2017
2017
Effective semi-supervised learning strategies for automatic sentence segmentation
2016
2016
Understanding Information Diffusion under Interactions
Information diffusion in online social networks has attracted substantial research effort. Although recent models begin to… 
2014
2014
Ensemble-based semi-supervised learning approaches for imbalanced splice site datasets
Producing accurate classifiers depends on the quality and quantity of labeled data. The lack of labeled data, due to its… 
2013
2013
Video classification and recommendation based on affective analysis of viewers
2009
2009
Evaluating Retraining Rules for Semi-Supervised Learning in Neural Network Based Cursive Word Recognition
Training a system to recognize handwritten words is a task that requires a large amount of data with their correct transcription… 
2009
2009
Unlabelled extra data do not always mean extra performance for semi‐supervised fault prediction
Abstract: This research focused on investigating and benchmarking several high performance classifiers called J48, random forests… 
2009
2009
Label propagation via bootstrapped support vectors for semantic relation extraction between named entities
Highly Cited
2007
Highly Cited
2007
Software quality estimation with limited fault data: a semi-supervised learning perspective
We addresses the important problem of software quality analysis when there is limited software fault or fault-proneness data. A… 
2006
2006
A Hybrid Approach for the Acquisition of Information Extraction Patterns
In this paper we present a hybrid approach for the acquisition of syntactico-semantic patterns from raw text. Our approach co… 
2006
2006
Clustering-training for Data Stream Mining
Mining data streams has attracted much attention recently. Labeled samples needed by most current stream classification methods… 
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