| 期刊全稱 | Boosted Statistical Relational Learners | | 期刊簡稱 | From Benchmarks to D | | 影響因子2023 | Sriraam Natarajan,Kristian Kersting,Jude Shavlik | | 視頻video | http://file.papertrans.cn/190/189793/189793.mp4 | | 發(fā)行地址 | Includes supplementary material: | | 學科分類 | SpringerBriefs in Computer Science | | 圖書封面 |  | | 影響因子 | This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory to handle uncertainty. It provides an overview of the methods and the key assumptions that allow for adaptation to different models and real world applications.The models are highly attractive due to their compactness and comprehensibility but learning their structure is computationally intensive. To combat this problem, the authors review the use of functional gradients for boosting the structure and the parameters of statistical relational models. The algorithms have been applied successfully in several SRL settings and have been adapted to several real problems from Information extraction in text to medical problems. Including both context and well-tested applications, Boosting Statistical Relational Learning from Benchmarks to Data-Driven Medicine is designed for researchers and professionals in machine learning and data mining. Computer engineers or students interested in statistics, data management, or health informatics wi | | Pindex | Book 2014 |
The information of publication is updating
|
|
|Archiver|手機版|小黑屋|
派博傳思國際
( 京公網(wǎng)安備110108008328)
GMT+8, 2025-10-16 04:58
發(fā)表于 2025-3-21 16:22:56
