The article proposes a model based on Fisher's Linear Discriminant for domain adaptation, balancing source task classifiers and limited target task data. It demonstrates the effectiveness of the proposed model in exploiting bias-variance trade-offs. The study focuses on physiological prediction problems, emphasizing the popularity of FLD in low-resource settings. The paper discusses domain adaptation theory, task similarity measures, and generative assumptions for optimal classification. Simulations validate the proposed method's accuracy and explore the impact of different parameters on classifier performance.
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arxiv.org
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