পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| রেগুলারাইজড অনলাইন লার্নিং× | অনলাইন লার্নিং× | |
|---|---|---|
| ক্ষেত্র | যন্ত্র শিখন | যন্ত্র শিখন |
| পরিবার | Machine learning | Machine learning |
| উদ্ভবের বছর≠ | 2007–2013 | 1958–2000s |
| প্রবর্তক≠ | Xiao, L.; Shalev-Shwartz, S.; McMahan, H. B. et al. | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) |
| ধরন≠ | Online optimization framework with regularization | Learning paradigm (sequential model update) |
| মৌলিক উৎস≠ | Xiao, L. (2010). Dual Averaging Methods for Regularized Stochastic and Online Optimization. Journal of Machine Learning Research, 11, 2543–2596. link ↗ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ |
| অপর নাম | FTRL, Follow-the-Regularized-Leader, online regularized optimization, regularized dual averaging | incremental learning, sequential learning, streaming learning, online machine learning |
| সম্পর্কিত | 6 | 6 |
| 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. | Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight. |
| ScholarGateডেটাসেট ↗ |
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