Abstract
This work studies the influence of the percentage of positive instances on positive bags on the performance of multiple instance learning algorithms using support vector machines. There are several studies that compare the performance of different types of multiple instance learning algorithms in different datasets and the performance of these algorithms with the supervised learning counterparts. Nonetheless, none of them study the influence of having a low or high percentage of positive instances on the data that the classifiers are using to learn. Therefore, we have created a new image dataset with different percentages of positive instances from a dataset for pedestrian detection. Experimental results of the performance of mi-SVM and MI-SVM algorithms on an image annotation task are presented. The results show that higher percentages of positive instances increase the overall accuracy of classifiers based on the maximum bag margin formulation.
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Monteiro, N.B., Barreto, J.P., Gaspar, J. (2016). Influence of Positive Instances on Multiple Instance Support Vector Machines. In: Reis, L., Moreira, A., Lima, P., Montano, L., Muñoz-Martinez, V. (eds) Robot 2015: Second Iberian Robotics Conference. Advances in Intelligent Systems and Computing, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-319-27149-1_21
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DOI: https://doi.org/10.1007/978-3-319-27149-1_21
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