| 書目名稱 | Probabilistic Topic Models | | 副標(biāo)題 | Foundation and Appli | | 編輯 | Di Jiang,Chen Zhang,Yuanfeng Song | | 視頻video | http://file.papertrans.cn/757/756835/756835.mp4 | | 概述 | Shares the theoretical foundations of probabilistic topic models.Introduces the pipeline of topic modeling and related algorithms.Presents industrial practices of applying topic models to products | | 圖書封面 |  | | 描述 | .This book introduces readers to the theoretical foundation and application of topic models. It provides readers with efficient means to learn about the technical principles underlying topic models. More concretely, it covers topics such as fundamental concepts, topic model structures, approximate inference algorithms, and a range of methods used to create high-quality topic models. In addition, this book illustrates the applications of topic models applied in real-world scenarios. Readers will be instructed on the means to select and apply suitable models for specific real-world tasks, providing this book with greater use for the industry. Finally, the book presents a catalog of the most important topic models from the literature over the past decades, which can be referenced and indexed by researchers and engineers in related fields. We hope this book can bridge the gap between academic research and industrial application and help topic models play an increasingly effective role inboth academia and industry. ..This book offers a valuable reference guide for senior undergraduate students, graduate students, and researchers, covering the latest advances in topic models, and for ind | | 出版日期 | Book 2023 | | 關(guān)鍵詞 | Topic Model; Graphical Model; Bayesian Network; Natural Language Processing; Machine Learning | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-99-2431-8 | | isbn_softcover | 978-981-99-2433-2 | | isbn_ebook | 978-981-99-2431-8 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor |
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