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Found 4 papers, 2 papers with code
Advancing Supervised Learning with the Wave Loss Function: A Robust and Smooth Approach
To empirically showcase the effectiveness of the proposed Wave-SVM and Wave-TSVM, we evaluate them on benchmark UCI and KEEL datasets (with and without feature noise) from diverse domains.
HawkEye: Advancing Robust Regression with Bounded, Smooth, and Insensitive Loss Function
It is worth noting that the HawkEye loss function stands out as the first loss function in SVR literature to be bounded, smooth, and simultaneously possess an insensitive zone.
Support matrix machine: A review
However, a significant portion of the real-world data exists in matrix format, which is given as input to SVM by reshaping the matrices into vectors.
RoBoSS: A Robust, Bounded, Sparse, and Smooth Loss Function for Supervised Learning
In the domain of machine learning algorithms, the significance of the loss function is paramount, especially in supervised learning tasks.
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