Machine learningMachine learning
可解释支持向量机
可解释支持向量机（Explainable SVM）将训练好的支持向量机与事后解释性层（通常是SHAP或LIME）相结合，为单个预测生成特征级解释，并提供全局重要性排序。它在保留SVM判别能力的同时，满足了医学、金融和法律等高风险领域对透明度的要求。
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来源
- Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗
- Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). 'Why should I trust you?': Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. DOI: 10.1145/2939672.2939778 ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable Support Vector Machine (XAI-augmented SVM). ScholarGate. https://scholargate.app/zh/machine-learning/explainable-support-vector-machine
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