Personalizing EEG-based Affective Models with Transfer Learning 阅读
提出了个体到个体的两种方法 1）源域和目标域共享结构（TCA、KPCA） 2)训练多个个体的分类器，对分类器参数进行迁移。 基本方法：组合所有个体可用的数据作为训练数据，训练一个基类分类器线性SVM。 数据特征维度：62*310（导联*特征维度） TCA和KPCA：源域：14个个体中随机选取5000个样本，核选用线性核，分类器选用one vs one 策略。目标域剩余的一个人 Transducti
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相关文章
- 1. 迁移学习论文阅读：Transfer Learning via Learning to Transfer
- 2. 论文阅读笔记《Large-Scale Few-Shot Learning: Knowledge Transfer With Class Hierarchy》
- 3. Knowledge Distillation论文阅读（2）：Learning Efficient Object Detection Models with Knowledge Distillation
- 4. 读《Hands-On Transfer Learning with Python》初体验
- 5. 论文阅读：A Survey on Transfer Learning
- 6. transfer learning
- 7. Machine Learning & Deep Learning 论文阅读笔记
- 8. Partial Transfer Learning with Selective Adversarial Networks
- 9. Deep Learning Models on Kubernetes with GPUs
- 10. 阅读笔记：Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer