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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 31, NO. 7, JULY 2009 1165 Combining SlantedFrame Classifiers for Improved Based Arabic Handwriting Recognition Ray Hajj Mohamad,
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Combining slanted-frame classifiers is a technique used in machine learning to improve the accuracy and performance of classifiers by combining the outputs of multiple slanted-frame classifiers.
Researchers and data scientists who are working on classification tasks can utilize combining slanted-frame classifiers.
To fill out combining slanted-frame classifiers, one needs to train multiple slanted-frame classifiers on the dataset and then combine their predictions using techniques like majority voting or weighted averaging.
The purpose of combining slanted-frame classifiers is to leverage the diversity of multiple classifiers to improve the overall classification accuracy and robustness.
The combining slanted-frame classifiers should report the details of each individual slanted-frame classifier used, the combining method employed, and the final classification results.
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