基于SVM与孪生SVM的多任务学习综述，聚焦可解释性与小样本优势。
Multi-Task Learning Based on Support Vector Machines and Twin Support Vector Machines: A Comprehensive Survey
- 整合SVM/TWSVM在多任务中的共享表示与结构耦合策略
- 对比理论性质、优化方法与实证性能，涵盖视觉、文本等应用
- 揭示孪生SVM新拓展潜力，适合关注小数据可解释建模的研究者
多任务学习（MTL）通过联合训练相关任务，利用共享信息提升泛化能力、效率和鲁棒性，尤其适用于数据稀缺或高维场景。尽管深度学习主导近年研究，支持向量机（SVM）与孪生支持向量机（TWSVM）因具备可解释性、理论严谨性及在小样本下的有效性，仍具重要价值。本章系统综述基于SVM与TWSVM的多任务学习方法，重点分析共享表示、任务正则化与结构耦合策略。特别关注面向多任务场景的新兴TWSVM扩展，其前景良好但研究尚浅。从理论特性、优化策略与实证表现等方面比较各类模型，并讨论其在计算机视觉、自然语言处理与生物信息学中的应用。最后指出研究空白，展望构建可扩展、可解释且可靠的基于间隔的多任务学习框架的未来方向。本文为关注SVM与TWSVM驱动多任务学习的研究人员与实践者提供全面参考。
原文摘要 · Abstract (English)
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.
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