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- W4362711833 abstract "The image classification is the prime task of any machine learning system that operates with images such that it can classify the given input into a particular set by appropriate methods. Classification of numbers automatically is carried out in this work. This work mainly concentrates on the identification of house numbers to classify the houses. The applications include tax payments, etc. Hence, in order to classify such data XGboost is used. The motivation behind the use of XGboost is that based on the optimization that it has. It also has cross-validation which eradicates the involvement of the overfitting of the data. This is a supervised learning task. The model is built upon a series of labelled data points that are subsequently test on several unlabelled data points. The model that is built is refined iteratively to improve the classification. The main problem with the recognition of house number is the noise which is predominant. Hence, this work concentrates on the preprocessing of the data which is done twice to eliminate noise. Further XGBoosting and deep random forest approaches are utilised to improve the accuracy in recognition which utilizes less memory. Results show that the deep random forest algorithm outperforms other traditional approaches." @default.
- W4362711833 created "2023-04-09" @default.
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- W4362711833 date "2023-01-01" @default.
- W4362711833 modified "2023-09-26" @default.
- W4362711833 title "XG Boosting and Deep Random Forest Based House Number Detection" @default.
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- W4362711833 doi "https://doi.org/10.1007/978-981-19-9819-5_19" @default.
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