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- W2051431796 abstract "To deal with heterogeneous classification problem efficiently, each heterogeneous object was represented by a set of measurements obtained on different part of it, and the heterogeneous classification problem was reformulated in the framework of multi-instance learning (MIL). Based on a variant of count-based MIL assumption, a maximum count least squares support vector machine (maxc-LS-SVM) learning algorithm was developed. The algorithm was tested on a set of toy datasets. It was found that maxc-LS-SVM inherits all the sound characters of both LS-SVM and MIL framework. A comparison study between the proposed approach and the other two MIL approaches (i.e., mi-SVM and MI-SVM) was performed on a real wolfberry fruit spectral dataset. The results demonstrate that by formulating the heterogeneous classification problem as a MIL one, the heterogeneous classification problem can be solved by the proposed maxc-LS-SVM algorithm effectively." @default.
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- W2051431796 date "2015-01-01" @default.
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- W2051431796 title "Dealing with heterogeneous classification problem in the framework of multi-instance learning" @default.
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- W2051431796 doi "https://doi.org/10.1016/j.talanta.2014.09.007" @default.
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