A Semisupervised Domain Adaptation Framework Using Dynamic Distribution Alignment and Manifold Regularization

This research presents a semisupervised domain adaptation method (SDM), which helps AI classifiers work better when transferring knowledge from one data domain to another (like using a model trained on one type of data to work with a different type). The method addresses two main problems: limited labeled training data in the target domain and distribution divergence (differences in data patterns between source and target domains) by iteratively updating training data while balancing multiple objectives like structural risk and manifold consistency (geometric patterns in data).