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Semi-supervised genetic programming for classification
Proceedings of the 13th annual conference on Genetic and evolutionary computation, 2011Learning from unlabeled data provides innumerable advantages to a wide range of applications where there is a huge amount of unlabeled data freely available. Semi-supervised learning, which builds models from a small set of labeled examples and a potential large set of unlabeled examples, is a paradigm that may effectively use those unlabeled data ...
Filipe de Lima Arcanjo +4 more
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Manifold contraction for semi-supervised classification
Science China Information Sciences, 2010The generalization ability of classification is often closely related to both the intra-class compactness and the inter-class separability. Owing to the fact that many current dimensionality reduction methods, regarded as a pre-processor, often lead to the poor classification performance on real-life data, in this paper, a new data preprocessing ...
Enliang Hu, Songcan Chen, Xuesong Yin
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Sparse regularization for semi-supervised classification
Pattern Recognition, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mingyu Fan +3 more
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Convex Multiview Semi-Supervised Classification
IEEE Transactions on Image Processing, 2017In many practical applications, there are a great number of unlabeled samples available, while labeling them is a costly and tedious process. Therefore, how to utilize unlabeled samples to assist digging out potential information about the problem is very important. In this paper, we study a multiclass semi-supervised classification task in the context
Feiping Nie 0001 +2 more
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Classification by semi-supervised discriminative regularization
Neurocomputing, 2010Linear discriminant analysis (LDA) is a well-known dimensionality reduction method which can be easily extended for data classification. Traditional LDA aims to preserve the separability of different classes and the compactness of the same class in the output space by maximizing the between-class covariance and simultaneously minimizing the within ...
Wu, Fei +4 more
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Semi-supervised and Compound Classification of Network Traffic
2012 32nd International Conference on Distributed Computing Systems Workshops, 2012This paper presents a new semi-supervised method to effectively improve traffic classification performance when few supervised training data are available. Existing semi supervised methods label a large proportion of testing flows as unknown flows due to limited supervised information, which severely affects the classification performance.
Jun Zhang 0010 +3 more
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Ant Based Semi-supervised Classification
2010Semi-supervised classification methods make use of the large amounts of relatively inexpensive available unlabeled data along with the small amount of labeled data to improve the accuracy of the classification. This article presents a novel 'self-training' based semi-supervised classification algorithm using the property of aggregation pheromone found ...
Anindya Halder +2 more
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Semi-supervised Classification by Local Coordination
2010Graph-based methods for semi-supervised learning use graph to smooth the labels of the points. However, most of them are transductive thus can't give predictions for the unlabeled data outside the training set directly. In this paper, we propose an inductive graph-based algorithm that produces a classifier defined on the whole ambient space.
Gelan Yang +3 more
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Semi-Supervised Classification with Universum
Proceedings of the 2008 SIAM International Conference on Data Mining, 2008Dan Zhang 0007 +3 more
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A new graph-based semi-supervised method for surface defect classification
Robotics and Computer-Integrated Manufacturing, 2021Liang Gao, , Yucheng Wang
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