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- W4385846178 abstract "In this paper, we recognize objects within images by collecting information from a large number of random-sizepatches of the image. The different backgrounds accompany the foreground object demand to have a learning system toidentify each patch as belonging to the object category or to the background category. We strengthen a recent methodcalled Evolution-COnstructed (ECO), which is based on the ensemble learning approach which combines several weakclassifier. The improvement is relying on decreasing the overfitting problem. Two different improving ideas areproposed: 1) Pooling operation, which is applied to the weak classifiers data, 2) Random Forest algorithm, which combines the weak classifiers outcomes. Experimental results are reported for classification of 9 categories of Caltech-101 data sets and proved that our modifications boost the performance over the base method and other existing methods" @default.
- W4385846178 created "2023-08-16" @default.
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- W4385846178 date "2018-06-01" @default.
- W4385846178 modified "2023-10-07" @default.
- W4385846178 title "Representation Learning Framework of Object Recognition via Feature Construction" @default.
- W4385846178 doi "https://doi.org/10.21608/mjcis.2018.311995" @default.
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