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- W2583484366 abstract "AdaBoost is a popular ensemble method utilized in pattern recognition problems that are considered tough. Besides being a robust technique it does suffer from few limitations viz. size of training data and presence of noise in training data. In this context, we proposed a novel technique called Perspective Based Model (PBM) for ensemble creation in case of multispectral data analysis. In the present paper, we evaluate its performance in terms of classification accuracy against AdaBoost.M2. Preliminary results show higher accuracy through PBM compared to a single classifier but also a lower classification performance for PBM compared to AdaBoost.M2. An improved performance is also observed for PBM on adding new data features." @default.
- W2583484366 created "2017-02-10" @default.
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- W2583484366 date "2016-12-01" @default.
- W2583484366 modified "2023-09-25" @default.
- W2583484366 title "Comparison of Adaboost.M2 and perspective based model ensemble in multispectral image classification" @default.
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- W2583484366 doi "https://doi.org/10.1109/indicon.2016.7839090" @default.
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