Probabilistic Topic Models Foundation and Application /

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 in...

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Xehetasun bibliografikoak
Egile Nagusiak:	Jiang, Di (Egilea), Zhang, Chen (Egilea), Song, Yuanfeng (Egilea)
Erakunde egilea:	SpringerLink (Online service)
Formatua:	Baliabide elektronikoa eBook
Hizkuntza:	ingelesa
Argitaratua:	Singapore : Springer Nature Singapore : Imprint: Springer, 2023.
Edizioa:	1st ed. 2023.
Gaiak:	Natural language processing (Computer science). Machine learning. Artificial intelligence > Data processing. Computational linguistics. Computer science. Algorithms. Natural Language Processing (NLP). Machine Learning. Data Science. Computational Linguistics. Theory and Algorithms for Application Domains. Design and Analysis of Algorithms.
Sarrera elektronikoa:	https://doi.org/10.1007/978-981-99-2431-8
Etiketak:	Etiketa erantsi Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!

Aurkibidea:

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.

Probabilistic Topic Models Foundation and Application /

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