Abstract
In this paper, a new co-training style semi-supervised algorithm is proposed, which employs Bagging based multimodal perturbation to label the unlabeled data. In detail, through perturbing the training data, input attributes and learning parameters together, the algorithm generates accurate but diversity k-nearest neighbor classifiers. These classifiers are refined using unlabeled examples which are labeled if the other classifiers agree on the labeling. Experimental results show that the semi-supervised algorithm could effectively improve the classification generalization by utilizing the unlabeled data.
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Su, L., Liao, H., Yu, Z., Tang, J. (2009). Semi-supervised Learning with Multimodal Perturbation. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5551. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01507-6_74
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DOI: https://doi.org/10.1007/978-3-642-01507-6_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01506-9
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