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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2017

21.01.2017

Multiple Instance Learning via Semi-supervised Laplacian TSVM

verfasst von: Xizhan Gao, Quansen Sun, Haitao Xu

Erschienen in: Neural Processing Letters | Ausgabe 1/2017

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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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Metadaten
Titel
Multiple Instance Learning via Semi-supervised Laplacian TSVM
verfasst von
Xizhan Gao
Quansen Sun
Haitao Xu
Publikationsdatum
21.01.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2017
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9579-5

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