arXiv:2608. 00949v1 Announce Type: new Abstract: The pinball-loss support vector machine is robust, but its asymmetry parameter is usually fixed in advance.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2606. 05814v1 Announce Type: new Abstract: The support vector machine (SVM) is a widely used classifier, but choosing an appropriate loss function remains difficult.
arXiv:2606. 08797v1 Announce Type: cross Abstract: Decision-focused learning has shown great promise for addressing predict-then-optimize problems, particularly in the presence of under-specified models.
arXiv:2608. 11567v1 Announce Type: new Abstract: In real-world scenarios, the training data usually contains redundant features, label noise and feature noise, which provide severe challenges for the efficiency of machine learning methods.
The paper introduces Backward Kernel Herding, an algorithm that iteratively removes data points to create representative subsets for kernel learning, achieving performance comparable to state‑of‑the‑art methods while speeding up subsampling when the reduced size is less than half the original dataset. It also proposes Flexible Kernel Thinning, an extension that allows construction of subsets of any size, not just successive halvings, and demonstrates that this method often yields the best predictive performance. Experiments on Gaussian Processes and Kernel Support Vector Machines show that Backward Kernel Herding excels in training‑time efficiency, while Flexible Kernel Thinning offers superior predictive accuracy and competitive memory usage, emphasizing the need to choose a reduction strategy based on the desired trade‑off between performance, cost, and memory.
arXiv:2503. 12902v4 Announce Type: replace Abstract: Model trees provide an appealing way to perform interpretable machine learning for both classification and regression problems.
arXiv:2608. 14288v1 Announce Type: new Abstract: We propose multiple new convex losses for SVM and Neural Networks, applied to binary classification tasks.