Support Vector Machines

Also known as SVM

Support vector machines (SVMs) find decision boundaries that separate classes with a wide margin; kernels such as the Gaussian RBF and polynomial kernel extend them to nonlinear patterns. Related methods include support vector regression for continuous outcomes and one-class SVMs for anomaly detection, while robust losses, sparsity penalties, and twin or proximal formulations address noise and feature selection. Research also develops faster training and model selection through optimization methods, GPU computation, and low-rank approximations such as Nyström, and tests SVMs in settings from medical imaging to remote sensing. A central tension is that SVMs remain strong on many structured and tabular tasks, but performance depends heavily on kernel and preprocessing choices, while scalability, high-dimensional generalization, and reliable interpretation remain challenges.

Latest papers

42 papers8 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

GraphSVR: A Graph Convolutional Support Vector Regression Framework for Robust Spatiotemporal Air Pollution Forecasting
May 5, 2026Nourin Jahan, Muhammed Navas T, Tanujit Chakraborty +1Air Quality Prediction Support Vector Machines
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India · SAFIR, Sorbonne University, Abu Dhabi, UAE · SCAI - Sorbonne Cluster for Artificial Intelligence, Paris, France +1
A Comparison of Traditional Machine Learning Algorithms and LSTM-Based Deep Learning Models for Email Sentiment Analysis
May 5, 2026Virdio Samuel Saragih, Baruna Abirawa, Kartini Lovian Simbolon +3Sentiment Analysis Text Classification
Faculty of Science Sumatra Institute of Technology
Elite-Driven Support Vector Machines for Classification
Apr 28, 2026Mohammad Jafari Jozani, Bahram MoeinianfarBinary Classification Support Vector Machines
Department of Statistics, University of Manitoba, Winnipeg, Manitoba, CANADA, R3T2N2
Benchmarking Quantum Kernel Support Vector Machines Against Classical Baselines on Tabular Data: A Rigorous Empirical Study with Hardware Validation
Apr 20, 2026Siavash Kakavand, Christoph Strohmeyer, Michael SchlotterQuantum Feature Maps Quantum Kernel Methods
SHARE at FAU, Schaeffler Technologies AG & Co. KG, Herzogenaurach 91074, Bavaria, Germany
Structural interpretability in SVMs with truncated orthogonal polynomial kernels
Apr 16, 2026Víctor Soto-Larrosa, Nuria Torrado, Edmundo J. HuertasInterpretable ML Support Vector Machines
Dpto. de Biociencias, Facultad de Ciencias Biomédicas y de la Salud, Universidad Europea de Madrid, C/ Tajo, s/n, 28670 Villaviciosa de Odón, Madrid, Spain. · Dpto. de Matemáticas, Facultad de Ciencias, Universidad Autónoma de Madrid, C/ Francisco Tomás y Valiente, 7, 28049 Madrid, Spain. · Instituto de Ciencias Matemáticas (ICMAT), Campus de Cantoblanco UAM, C/ Nicolás Cabrera, 13–15, 28049 Madrid, Spain. +1
Sparse $ε$ insensitive zone bounded asymmetric elastic net support vector machines for pattern classification
Apr 9, 2026Haiyan Du, Hu YangSupport Vector Machines Robust Loss Functions
College of Mathematics and Statistics, Chongqing University, Chongqing, 401331, China
Gap Safe Screening Rules for Fast Training of Robust Support Vector Machines under Feature Noise
Mar 26, 2026Tan-Hau Nguyen, Thu-Le Tran, Kien Trung NguyenSupport Vector Machines Robust Optimization
Can Tho University, Vietnam
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.
Robust support vector model based on bounded asymmetric elastic net loss for binary classification
Mar 6, 2026Haiyan Du, Hu YangRobust Loss Functions Support Vector Machines
College of Mathematics and Statistics, Chongqing University, Chongqing, 401331, China
OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection
Jul 25, 2025Nicolas Pinon, Robin Trombetta, Carole LartizienMedical Image Anomaly Detection Unsupervised Representation Learning
MYRIAD
Physics-Informed Support Vector Kernels via Green-Function Analogies and Jackson-Chebyshev Spectral Design
Feb 16, 2025Nan-Hong Kuo, Renata WongSupport Vector Machines Kernel Methods
Department of Physics, National Taiwan University, Taipei, Taiwan · Department of Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan · Department of Neurology, Chang Gung Memorial Hospital, Keelung, Taiwan +1
Classification of Financial Data Using Quantum Support Vector Machine
Dec 14, 2024Seemanta Bhattacharjee, MD. Muhtasim Fuad, A. K. M. Fakhrul HossainQuantitative Finance Hybrid Quantum-Classical ML
Shahjalal University Of Science and Technology, Sylhet-3114, Bangladesh

May 5, 2026J/K move · Enter open · S save

GraphSVR: A Graph Convolutional Support Vector Regression Framework for Robust Spatiotemporal Air Pollution Forecasting

Nourin Jahan, Muhammed Navas T, Tanujit Chakraborty, Madhurima Panja

Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India · SAFIR, Sorbonne University, Abu Dhabi, UAE · SCAI - Sorbonne Cluster for Artificial Intelligence, Paris, France+1

Air Quality Prediction Support Vector Machines Spatiotemporal Forecasting Spatiotemporal GNNs

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Urban air quality forecasting is challenging because pollutant concentrations are nonlinear, nonstationary, spatiotemporally dependent, and often affected by anomalous observations caused by traffic congestion, industrial emissions, and seasonal meteorological variability. This study proposes a Graph Convolutional Support Vector Regression (GraphSVR) framework for robust spatiotemporal forecasting of urban air pollution. The model combines graph convolutional learning to capture inter-station spatial dependence with support vector regression to model nonlinear temporal dynamics while reducing sensitivity to outlier observations. The proposed framework is evaluated using air quality records from 37 monitoring stations in Delhi and 18 stations in Mumbai, representing inland and coastal metropolitan environments in India. Forecasting performance is assessed across multiple horizons and compared with established temporal and spatiotemporal benchmarks. The results show that GraphSVR consistently improves predictive accuracy and maintains stable performance across seasons and outlier-prone pollution episodes. Statistical test further confirms the reliability of the proposed approach across the datasets. Furthermore, the conformal prediction approach is integrated with GraphSVR to generate calibrated prediction intervals, enhancing its practical value for uncertainty-aware air quality monitoring and public health decision-making.