کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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410701 | 679160 | 2011 | 6 صفحه PDF | دانلود رایگان |

The upper integral is a type of non-linear integral with respect to non-additive measures, which represents the maximum potential of efficiency for a group of features with interaction. The value of upper integrals can be evaluated through solving a linear programming problem. Considering the upper integral as a classifier, this paper first investigates its implementation and performance. Fusing multiple upper integral classifiers together by using a single layer neural network, this paper considers a upper integral network as a classification system. The learning mechanism of ELM is used to train this single layer neural network. A comparison of performance between a single upper integral classifier and the upper integral network is given on a number of benchmark databases.
Journal: Neurocomputing - Volume 74, Issue 16, September 2011, Pages 2520–2525