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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… 
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Active learning (machine learning)Classic monolingual word-sense disambiguationCo-trainingConcept learning

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Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2017
2017
Learning a Discriminative Distance Metric With Label Consistency for Scene Classification
To achieve high scene classification performance of high spatial resolution remote sensing images (HSR-RSIs), it is important to… 
2012
2012
Low-energy Formulations of Support Vector Machine Kernel Functions for Biomedical Sensor Applications
Although physiologically-indicative signals can be acquired in low-power biomedical sensors, their accurate analysis imposes… 
2010
2010
Semi-supervised Learning for SVM-KNN
Compared with labeled data, unlabeled data are significantly easier to obtain. Currently, classification of unlabeled data is an… 
2010
2010
Semi-supervised Learning for Opinion Detection
Research on opinion detection has shown that a large number of opinion-labeled data are necessary for capturing subtle opinions… 
2010
2010
Semi-supervised incremental learning
The paper introduces a hybrid evolving architecture for dealing with incremental learning. It consists of two components… 
2008
2008
Normalized LDA for semi-supervised learning
Linear Discriminant Analysis (LDA) has been a popular method for feature extracting and face recognition. As a supervised method… 
2008
2008
Semi-supervised Classification Using Local and Global Regularization
In this paper, we propose a semi-supervised learning (SSL) algorithm based on local and global regularization. In the local… 
2003
2003
OLERA: A Semi-supervised Approach for Web Data Extraction with Visual Support
Information extraction (IE) from semi-structured Web documents plays an important role for a variety of information agents. Over… 
2002
2002
Discovering Frequent Substructures from Hierarchical Semi-structured Data
Frequent substructure discovery from a collection of semi-structured objects can serve for storage, browsing, querying, indexing… 
1998
1998
Value Profiling for Instructions and Memory Locations
OF THE THESIS Value Pro ling for Instructions and Memory Locations by Peter Feller Master of Science in Computer Engineering…