| 書目名稱 | Improved Classification Rates for Localized Algorithms under Margin Conditions |
| 編輯 | Ingrid Karin Blaschzyk |
| 視頻video | http://file.papertrans.cn/463/462738/462738.mp4 |
| 概述 | Study in the field of natural sciences.Study in the field of statistical learning theory |
| 圖書封面 |  |
| 描述 | Support vector machines (SVMs) are one of the most successful algorithms on small and medium-sized data sets, but on large-scale data sets their training and predictions become computationally infeasible. The author considers a spatially defined data chunking method for large-scale learning problems, leading to so-called localized SVMs, and implements an in-depth mathematical analysis with theoretical guarantees, which in particular include classification rates. The statistical analysis relies on a new and simple partitioning based technique and takes well-known margin conditions into account that describe the behavior of the data-generating distribution. It turns out that the rates outperform known rates of several other learning algorithms under suitable sets of assumptions. From a practical point of view, the author shows that a common training and validation procedure achieves the theoretical rates adaptively, that is, without knowing the margin parameters in advance. |
| 出版日期 | Book 2020 |
| 關(guān)鍵詞 | Classification; Learning Rates; Gaussian Kernel; Tsybakov Noise; Localized SVMs; Support Vector Machines |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-658-29591-2 |
| isbn_softcover | 978-3-658-29590-5 |
| isbn_ebook | 978-3-658-29591-2 |
| copyright | Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 |
|Archiver|手機版|小黑屋|
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