পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| অনলাইন সাপোর্ট ভেক্টর মেশিন× | অনলাইন লার্নিং× | |
|---|---|---|
| ক্ষেত্র | যন্ত্র শিখন | যন্ত্র শিখন |
| পরিবার | Machine learning | Machine learning |
| উদ্ভবের বছর≠ | 2005–2011 | 1958–2000s |
| প্রবর্তক≠ | Shalev-Shwartz, Singer, et al. (Pegasos); Bordes, Bottou et al. (LASVM) | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) |
| ধরন≠ | Online kernel classifier | Learning paradigm (sequential model update) |
| মৌলিক উৎস≠ | Shalev-Shwartz, S., Singer, Y., Srebro, N., & Cotter, A. (2011). Pegasos: Primal estimated sub-gradient solver for SVM. Mathematical Programming, 127(1), 3–30. DOI ↗ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ |
| অপর নাম | Online SVM, Incremental SVM, LASVM, Pegasos SVM | incremental learning, sequential learning, streaming learning, online machine learning |
| সম্পর্কিত≠ | 3 | 6 |
| সারসংক্ষেপ≠ | Online SVM adapts the classical support vector machine to streaming or sequentially arriving data by updating the decision boundary one example at a time rather than solving a global quadratic program. Algorithms such as Pegasos and LASVM make this tractable at large scale, preserving the margin-maximising spirit of SVMs with sub-linear time per update. | 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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