পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| রেগুলারাইজড অনলাইন লার্নিং× | ট্রান্সফার লার্নিং× | |
|---|---|---|
| ক্ষেত্র | যন্ত্র শিখন | যন্ত্র শিখন |
| পরিবার | Machine learning | Machine learning |
| উদ্ভবের বছর≠ | 2007–2013 | 2010 (formalized); 1990s (early roots) |
| প্রবর্তক≠ | Xiao, L.; Shalev-Shwartz, S.; McMahan, H. B. et al. | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| ধরন≠ | Online optimization framework with regularization | Learning paradigm |
| মৌলিক উৎস≠ | Xiao, L. (2010). Dual Averaging Methods for Regularized Stochastic and Online Optimization. Journal of Machine Learning Research, 11, 2543–2596. link ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| অপর নাম | FTRL, Follow-the-Regularized-Leader, online regularized optimization, regularized dual averaging | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| সম্পর্কিত≠ | 6 | 3 |
| সারসংক্ষেপ≠ | Regularized online learning extends the online learning paradigm by incorporating a regularization penalty into each weight update, controlling model complexity while processing data one example at a time. Algorithms such as Follow-the-Regularized-Leader (FTRL) and Regularized Dual Averaging (RDA) make this approach practical at scale, enabling sparse, well-calibrated models on streaming data. | Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond. |
| ScholarGateডেটাসেট ↗ |
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