Multi-Task Learning Based on Support Vector Machines and Twin Support Vector Machines: A Comprehensive Survey

TL;DR

This survey reviews multi-task learning approaches based on Support Vector Machines and Twin Support Vector Machines, emphasizing their theoretical foundations, recent extensions, and applications across various fields, while identifying future research directions.

Contribution

It provides a comprehensive overview of SVM and TWSVM-based multi-task learning methods, highlighting recent extensions and discussing future research challenges.

Findings

TWSVM extensions show promise for multi-task learning.

Comparison of models in terms of theoretical and empirical performance.

Applications span computer vision, NLP, and bioinformatics.

Abstract

Multi-task learning (MTL) enables simultaneous training across related tasks, leveraging shared information to improve generalization, efficiency, and robustness, especially in data-scarce or high-dimensional scenarios. While deep learning dominates recent MTL research, Support Vector Machines (SVMs) and Twin SVMs (TWSVMs) remain relevant due to their interpretability, theoretical rigor, and effectiveness with small datasets. This chapter surveys MTL approaches based on SVM and TWSVM, highlighting shared representations, task regularization, and structural coupling strategies. Special attention is given to emerging TWSVM extensions for multi-task settings, which show promise but remain underexplored. We compare these models in terms of theoretical properties, optimization strategies, and empirical performance, and discuss applications in fields such as computer vision, natural language processing, and bioinformatics. Finally, we identify research gaps and outline future directions for building scalable, interpretable, and reliable margin-based MTL frameworks. This work provides a comprehensive resource for researchers and practitioners interested in SVM- and TWSVM-based multi-task learning.