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Privileged Semi-Supervised Learning
2018 25th IEEE International Conference on Image Processing (ICIP), 2018Semi-Supervised Learning (SSL) aims to leverage unlabeled data to improve performance. Due to the lack of supervised information, previous works mainly focus on how to utilize the available unlabeled data to improve the training quality. However, the estimation of the data distribution revealed by the unlabeled examples might not be accurate as their ...
Xingyu Chen +4 more
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Reliable Semi-supervised Learning
2016 IEEE 16th International Conference on Data Mining (ICDM), 2016In this paper, we propose a Reliable Semi-Supervised Learning framework, called ReSSL, for both static and streaming data. Instead of relaxing different assumptions, we do model the reliability of cluster assumption, quantify the distinct importance of clusters (or evolving micro-clusters on data streams), and integrate the cluster-level information ...
Junming Shao +3 more
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On semi-supervised learning and sparsity
2009 IEEE International Conference on Systems, Man and Cybernetics, 2009In this article we establish a connection between semi-supervised learning and compressive sampling. We show that sparsity and compressibility of the learning function can be obtained from heavy-tailed distributions of filter responses or coefficients in spectral decompositions.
Alexander Balinsky, Helen Balinsky
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Budget Semi-supervised Learning
2009In this paper we propose to study budget semi-supervised learning , i.e., semi-supervised learning with a resource budget, such as a limited memory insufficient to accommodate and/or process all available unlabeled data. This setting is with practical importance because in most real scenarios although there may exist abundant unlabeled data, the ...
Zhi-Hua Zhou +3 more
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Semi-supervised learning by disagreement
2008 IEEE International Conference on Granular Computing, 2008In real-world applications, assigning labels to examples usually requires human effort and therefore, labeled training examples are expensive; unlabeled training examples, however, are cheap and abundant. As a consequence, semi-supervised learning which attempts to exploit unlabeled data to help improve learning performance has become a very hot topic ...
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2006
In recent years, there has been considerable interest in non-standard learning problems, namely in the so-called semi-supervised learning scenarios. Most formulations of semisupervised learning see the problem from one of two (dual) perspectives: supervised learning (namely, classification) with missing labels; unsupervised learning (namely, clustering)
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In recent years, there has been considerable interest in non-standard learning problems, namely in the so-called semi-supervised learning scenarios. Most formulations of semisupervised learning see the problem from one of two (dual) perspectives: supervised learning (namely, classification) with missing labels; unsupervised learning (namely, clustering)
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Semi-supervised Learning with Transfer Learning
2013Traditional machine learning works well under the assumption that the training data and test data are in the same distribution. However, in many real-world applications, this assumption does not hold. The research of knowledge transfer has received considerable interest recently in Natural Language Processing to improve the domain adaptation of machine
Huiwei Zhou +3 more
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Semi-supervised learning with an imperfect supervisor
Knowledge and Information Systems, 2005Real-life applications may involve huge data sets with misclassified or partially classified training data. Semi-supervised learning and learning in the presence of label noise have recently emerged as new paradigms in the machine learning community to cope with this kind of problems.
Amini, Massih-Reza, Gallinari, Patrick
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Semi-Supervised Bilinear Subspace Learning
IEEE Transactions on Image Processing, 2009Recent research has demonstrated the success of tensor based subspace learning in both unsupervised and supervised configurations (e.g., 2-D PCA, 2-D LDA, and DATER). In this correspondence, we present a new semi-supervised subspace learning algorithm by integrating the tensor representation and the complementary information conveyed by unlabeled data.
Dong Xu 0001, Shuicheng Yan
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Boosting for multiclass semi-supervised learning
Pattern Recognition Letters, 2014We present an algorithm for multiclass semi-supervised learning, which is learning from a limited amount of labeled data and plenty of unlabeled data. Existing semi-supervised learning algorithms use approaches such as one-versus-all to convert the multiclass problem to several binary classification problems, which is not optimal.
Jafar Tanha +2 more
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