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Multiple Instance Learning via Semi-supervised Laplacian TSVM

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Abstract

Multiple instance learning attempts to learn from a training set consists of labeled bags each containing many unlabeled instances. In previous works, most existing algorithms mainly pay attention to the ‘most positive’ instance in each positive bag, but ignore the other instances. For utilizing these unlabeled instances in positive bags, we present a new multiple instance learning algorithm via semi-supervised laplacian twin support vector machines (called Miss-LTSVM). In Miss-LTSVM, all instances in positive bags are used in the manifold regularization terms for improving the performance of classifier. For verifying the effectiveness of the presented method, a series of comparative experiments are performed on seven multiple instance data sets. Experimental results show that the proposed method has better classification accuracy than other methods in most cases.

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Acknowledgements

This work is supported by the National Science Foundation of China under Grant Nos. 61273251 and 61673220.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Xizhan Gao & Quansen Sun

  2. School of Mathematical Sciences, Liaocheng University, Liaocheng, Shandong, People’s Republic of China

    Haitao Xu

Authors
  1. Xizhan Gao
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  2. Quansen Sun
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  3. Haitao Xu
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Corresponding author

Correspondence to Quansen Sun.

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Gao, X., Sun, Q. & Xu, H. Multiple Instance Learning via Semi-supervised Laplacian TSVM. Neural Process Lett 46, 219–232 (2017). https://doi.org/10.1007/s11063-017-9579-5

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