পদ্ধতির তুলনা করুন

	নিয়মিত স্থানান্তরণ শিখন (Regularized Transfer Learning)	মেট্রিক লার্নিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2000s–2010s	2003 (foundational); refined 2009 (LMNN)
প্রবর্তক	Pan, S. J. & Yang, Q. (survey); regularization variants by multiple authors	Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.
ধরন	Regularized supervised/semi-supervised learning framework	Representation learning / supervised distance optimization
মৌলিক উৎস	Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	Xing, E. P., Jordan, M. I., Russell, S., & Ng, A. Y. (2003). Distance metric learning with application to clustering with side-information. In Advances in Neural Information Processing Systems (NIPS), 16, 505–512.
অপর নাম	regularized domain adaptation, transfer learning with regularization, penalized transfer learning, regularized fine-tuning	Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance
সম্পর্কিত	6	5
সারসংক্ষেপ	Regularized Transfer Learning applies explicit penalty terms to a transfer learning pipeline to control how much a model shifts away from source-domain knowledge when adapting to a new target domain. The regularizer discourages negative transfer — the harmful carry-over of irrelevant source patterns — while preserving beneficial shared representations and preventing overfitting when target-domain labels are scarce.	Metric learning is a machine-learning framework that trains a distance or similarity function from data so that semantically similar examples end up close together in the learned space while dissimilar examples are pushed apart. Unlike fixed distances such as Euclidean, the learned metric adapts to the structure of the task, making downstream classifiers, clusterers, and retrieval systems significantly more accurate.