Mendeley出力
Probabilistic Topic Models : Foundation and Application / by Di Jiang, Chen Zhang, Yuanfeng Song
| データ種別 | 電子ブック |
|---|---|
| 版 | 1st ed. 2023. |
| 出版者 | (Singapore : Springer Nature Singapore : Imprint: Springer) |
| 出版年 | 2023 |
| 大きさ | X, 149 p. 1 illus : online resource |
| 著者標目 | *Jiang, Di author Zhang, Chen author Song, Yuanfeng author SpringerLink (Online service) |
書誌詳細を非表示
| 一般注記 | Chapter 1. Basics -- Chapter 2. Topic Models -- 3. Chapter 3. Pre-processing of Training Data -- Chapter 4. Expectation Maximization -- Chapter 5. Markov Chain Monte Carlo Sampling -- Chapter 6. Variational Inference -- Chapter 7. Distributed Training -- Chapter 8. Parameter Setting -- Chapter 9. Topic Deduplication and Model Compression -- Chapter 10. Applications 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 in both 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 industrial practitioners, sharing state-of-the-art solutions for topic-related applications. The book can also serve as a reference for job seekers preparing for interviews HTTP:URL=https://doi.org/10.1007/978-981-99-2431-8 |
|---|---|
| 件 名 | LCSH:Natural language processing (Computer science) LCSH:Machine learning LCSH:Artificial intelligence—Data processing LCSH:Computational linguistics LCSH:Computer science LCSH:Algorithms FREE:Natural Language Processing (NLP) FREE:Machine Learning FREE:Data Science FREE:Computational Linguistics FREE:Theory and Algorithms for Application Domains FREE:Design and Analysis of Algorithms |
| 分 類 | LCC:QA76.9.N38 DC23:006.35 |
| 書誌ID | EB00002183 |
| ISBN | 9789819924318 |
類似資料
この資料の利用統計
このページへのアクセス回数：10回
※2019年3月27日以降
全貸出数：0回
(1年以内の貸出：0回)
※2019年3月27日以降