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• W2023449623 abstract "Multi-instance learning (MIL) has received more and more attentions in the machine learning research field due to its theoretical interest and its applicability to diverse real-world problems. In this paper, we present a probabilistic kernel approach for the multi-instance learning problems with various multi-instance assumptions by imposing Gaussian process prior on an unobservable latent function defined on the instance space. Because the relationship between the bag and its instances, triggered by the multi-instance assumption, can be exactly captured by defining the likelihood function, we can deal with different multi-instance assumptions by employing different likelihood functions. Experimental results on several multi-instance problems show that the proposed algorithms are valid and can achieve superior performance to the published MIL algorithms." @default.
• W2023449623 created "2016-06-24" @default.
• W2023449623 creator A5031134747 @default.
• W2023449623 creator A5071493261 @default.
• W2023449623 creator A5091763401 @default.
• W2023449623 date "2013-01-01" @default.
• W2023449623 modified "2023-09-26" @default.
• W2023449623 title "A probabilistic kernel approach for solving the multi-instance learning problems with different assumptions" @default.
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• W2023449623 doi "https://doi.org/10.1504/ijaom.2013.055881" @default.
• W2023449623 hasPublicationYear "2013" @default.
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