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008			230608s2023 si | o |||| 0|eng d
020			_a9789819924318
024	7		_a10.1007/978-981-99-2431-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.N38 _b2023 EB
100	1		_aJiang, Di _eautor _4http://id.loc.gov/vocabulary/relators/aut _9689585
245	1	0	_aProbabilistic Topic Models : _bFoundation and Application _cby Di Jiang, Chen Zhang, Yuanfeng Song
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aChapter 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.
520			_aThis 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.
988			_aSpringer_Computer_2023
650		7	_2embne _9158738 _aProceso en lenguaje natural (Informática)
700	1		_9690188 _aSong, Yuanfeng _eautor
700	1		_9690189 _aZhang, Chen _eautor _d1984-
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-99-2431-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _ek _zSI