Tag
This paper proposes a new asymmetric robust bounded sparse smooth (aR) loss function for l1-norm penalized geometric twin support vector machine (aRSGTSVM) to handle classification and regression tasks with label and feature noise, achieving feature selection and robustness. Experiments on synthetic and UCI datasets plus China stock market index tracking demonstrate superiority.
This paper proposes a data-driven elastic-net support vector machine that learns simplex-constrained weights over candidate pinball losses, with a distributed solver for vertically partitioned high-dimensional data. Theoretical guarantees and experiments demonstrate equivalence to centralized training under common initialization.
Introduces Support Vector Attention (SV-Attention), a trainable max-margin memory that provides certified selection of tokens with zero weight and exact unlearning via a reversible incremental solver. It achieves improved rare-item recall and patient-record deletion capabilities.