logo
Product categories

EbookNice.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link.  https://ebooknice.com/page/post?id=faq


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookNice Team

(Ebook) Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series) by Lise Getoor; Ben Taskar ISBN 9780262072885, 0262072882

  • SKU: EBN-10008976
Zoomable Image
$ 32 $ 40 (-20%)

Status:

Available

5.0

29 reviews
Instant download (eBook) Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series) after payment.
Authors:Lise Getoor; Ben Taskar
Pages:796 pages.
Year:2007
Editon:Hardcover
Publisher:MIT Press
Language:english
File Size:8.96 MB
Format:pdf
ISBNS:9780262072885, 0262072882
Categories: Ebooks

Product desciption

(Ebook) Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series) by Lise Getoor; Ben Taskar ISBN 9780262072885, 0262072882

Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.Lise Getoor is Assistant Professor in the Department of Computer Science at the University of Maryland. Ben Taskar is Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania.
*Free conversion of into popular formats such as PDF, DOCX, DOC, AZW, EPUB, and MOBI after payment.

Related Products