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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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CoBoostingComputer scienceCoupled pattern learnerFunctional genomics

Papers overview

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2017
2017
Weighted Co-Training for Cross-Domain Image Sentiment Classification
Image sentiment classification, which aims to predict the polarities of sentiments conveyed by the images, has gained a lot of… 
2017
2017
Using eigenvoices and nearest-neighbours in HMM-based cross-lingual speaker adaptation with limited data (Sınırlı veriyle HMM tabanlı çapraz-dil konuşmacı uyarlamasında özses ve en yakın komşu kullan…
—Cross-lingual speaker adaptation for speech synthesis has many applications, such as use in speech-to-speech translation systems… 
2016
2016
Understanding Information Diffusion under Interactions
Information diffusion in online social networks has attracted substantial research effort. Although recent models begin to… 
2012
2012
Semi-automatic Annotation of Chinese Word Structure
Chinese word structure annotation is potentially useful for many NLP tasks, especially for Chinese word segmentation. Li and Zhou… 
2012
2012
Robot self-awareness: usage of co-training for distance functions for sequences of images
We use temporal relation based data mining to consider robot selfawareness. We consider the problem of finding regularities among… 
2011
2011
Learning from Chinese-English Parallel Data for Chinese Tense Prediction
Tense prediction can be useful for many language processing tasks, such as temporal inference and machine translation. In this… 
2010
2010
Semi-supervised Kernel Based Progressive SVM
Most existing semi-supervised methods implemented either the cluster assumption or the manifold assumption. The performance will… 
2009
2009
Co-Training Semi-Supervised Active Learning Algorithm with Noise Filter
The classification performance of the classifier based on semi-supervised learning is weakened when the noise samples are… 
2008
2008
Benchmarking Noun Compound Interpretation
In this paper we provide benchmark results for two classes of methods used in interpreting noun compounds (NCs): semantic… 
2005
2005
Labeling of Textured Data with Co-training and Active Learning
In this paper, we present a robust texture labeling method that requires minimum user interaction. Initially only a fraction of…