Robust Classification

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36 papers3 papers in the last four weeks, down 40% on the four weeks before. 0.0% of all new papers.

A Fractional Fox H-Function Kernel for Support Vector Machines: Robust Classification via Weighted Transmutation Operators
Mar 13, 2026Gustavo DorregoRobust Classification Kernel Methods
Departamento de Matemática, Facultad de Ciencias Exactas y Naturales y Agrimensura, Universidad Nacional del Nordeste, Corrientes, Argentina.
The Price of Robustness: Stable Classifiers Need Overparameterization
Mar 3, 2026Jonas von Berg, Adalbert Fono, Massimiliano Datres +2Overparameterization Robust Classification
Ludwig-Maximilians-Universität München Munich, Germany Munich Center for Machine Learning (MCML) · ∗now at Aleph Alpha Research · †University of Tromso, DLR-German Aerospace Center
Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits
Feb 17, 2026Gilad Nurko, Roi Benita, Yehoshua Dissen +4Robust Classification Diffusion Models
Technion – Israel Institute of Technology, Haifa, Israel · NTT, Inc., Japan.
destroR: A Benchmark and Adversarial-Training Defense for Bangla Transfer Models under Meaning-Preserving Attacks
Nov 13, 2025Saadat Rafid Ahmed, Rubayet Shareen, Radoan Sharkar +3Adversarial Text Attacks Adversarial Robustness of Language Models
Dept. of Computer Science and Engineering, BRAC University
Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces
Dec 29, 2024Linglingzhi Zhu, Yunqin Zhu, Yao XieDistributionally Robust Optimization Wasserstein Distributionally Robust Optimization
1H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology
Large Pretraining Datasets Don't Guarantee Robustness after Fine-Tuning in Image Classification
Oct 28, 2024Jaedong Hwang, Brian Cheung, Zhang-Wei Hong +3Neural Network Robustness OOD Generalization
Massachusetts Institute of Technology

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A Fractional Fox H-Function Kernel for Support Vector Machines: Robust Classification via Weighted Transmutation Operators

Gustavo Dorrego

Departamento de Matemática, Facultad de Ciencias Exactas y Naturales y Agrimensura, Universidad Nacional del Nordeste, Corrientes, Argentina.

Robust Classification Kernel Methods Support Vector Machines

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Support Vector Machines (SVMs) rely heavily on the choice of the kernel function to map data into high-dimensional feature spaces. While the Gaussian Radial Basis Function (RBF) is the industry standard, its exponential decay makes it highly susceptible to structural noise and outliers, often leading to severe overfitting in complex datasets. In this paper, we propose a novel class of non-stationary kernels derived from the fundamental solution of the generalized time-space fractional diffusion-wave equation. By leveraging a structure-preserving transmutation method over Weighted Sobolev Spaces, we introduce the Amnesia-Weighted Fox Kernel, an exact analytical Mercer kernel governed by the Fox H-function. Unlike standard kernels, our formulation incorporates an aging weight function (the "Amnesia Effect") to penalize distant outliers and a fractional asymptotic power-law decay to allow for robust, heavy-tailed feature mapping (analogous to Lévy flights). Numerical experiments on both synthetic datasets and real-world high-dimensional radar data (Ionosphere) demonstrate that the proposed Amnesia-Weighted Fox Kernel consistently outperforms the standard Gaussian RBF baseline, reducing the classification error rate by approximately 50% while maintaining structural robustness against outliers.

Latest papers

A Fractional Fox H-Function Kernel for Support Vector Machines: Robust Classification via Weighted Transmutation Operators

The Price of Robustness: Stable Classifiers Need Overparameterization

Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits

destroR: A Benchmark and Adversarial-Training Defense for Bangla Transfer Models under Meaning-Preserving Attacks

Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces

Large Pretraining Datasets Don't Guarantee Robustness after Fine-Tuning in Image Classification