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Nonage manuscript No. (will be inserted by the editor) Non-Parametric Kernel Learning with Robust Pairwise Constraints Chang you Chen Jumping Zhang Elephant He Zhi-Hua Zhou Received: date / Accepted:
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Non-parametric kernel learning is a machine learning technique that allows the algorithm to learn the optimal function form from the data rather than making assumptions about the underlying distribution.
Individuals or organizations utilizing non-parametric kernel learning techniques for their data analysis are required to file non-parametric kernel learning forms.
To fill out non-parametric kernel learning forms, one needs to provide information about the specific kernel function used, the training dataset, and any specific parameter choices made during the learning process.
The purpose of non-parametric kernel learning is to allow the algorithm to capture complex patterns in data without making strong assumptions about the underlying distribution, making it suitable for a wide range of applications.
Non-parametric kernel learning forms typically require details such as the dataset used, the kernel function employed, selected hyperparameters, performance metrics, and any relevant preprocessing or postprocessing steps taken during the learning process.
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