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Probabilistic Topic Models

Foundation and Application
BuchKartoniert, Paperback
149 Seiten
Englisch
Springererschienen am09.06.20242023
This book introduces readers to the theoretical foundation and application of 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.mehr
Verfügbare Formate
BuchGebunden
EUR181,89
BuchKartoniert, Paperback
EUR181,89
E-BookPDF1 - PDF WatermarkE-Book
EUR171,19

Produkt

KlappentextThis book introduces readers to the theoretical foundation and application of 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.
Details
ISBN/GTIN978-981-99-2433-2
ProduktartBuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2024
Erscheinungsdatum09.06.2024
Auflage2023
Seiten149 Seiten
SpracheEnglisch
Gewicht254 g
IllustrationenX, 149 p. 1 illus.
Artikel-Nr.56450911

Inhalt/Kritik

Inhaltsverzeichnis
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.mehr

Autor

Di Jiang works as principal scientist and engineering manager at WeBank AI. His research interests include text mining, speech processing, and the broader topics of artificial intelligence. He obtained his PhD degree from the Hong Kong University of Science and Technology. He has served on the technical program committee of various international conferences, including KDD, AAAI, IJCAI, DASFAA, CIKM, and COLING. He is serving as a reviewer of international journals including TIST, TKDE, TWEB, etc. His work won DASFAA 2013 Best Student Paper Runner-up, Yahoo TechPulse 2014 Best Paper Award, and CCF Science and Technology Award.

Chen Zhang is currently a research assistant professor in the Department of Computing, PolyU. Before joining the Department, he worked as a senior manager of the Big Data Institute at The Hong Kong University of Science and Technology (HKUST). He received his PhD in Computer Science and Engineering from HKUST in 2015, supervised by Prof. Lei CHEN. Dr.Zhang has served on the Technical Program Committee of various international conferences, including ICDE and CIKM. He won the Outstanding Demonstration Award at VLDB 2014. He has published various papers at top-tier conferences and journals such as ICDE, VLDB, SIGMOD, and TKDE. He is broadly interested in crowdsourcing, fintech, NLP, and interdisciplinary research.

Yuanfeng Song works as a research engineer at AI Group, WeBank. He has more than ten years of working experience in leading Internet institutions such as Tencent and Baidu. His research interests include natural language processing and speech recognition. He received an MPhil degree in computer science from the Hong Kong University of Science and Technology in 2012. He has served as a reviewer of international conferences and journals, including AAAI, EMNLP, and KBS. He has published various papers in top conferences and journals such as KDD, MM, ICDM, TOIS, and TOIST.