Introduction to Statistical Relational Learning (Adaptive Computation and Maching Learning)

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(as of Jan 19,2025 15:30:30 UTC – Details)

Publisher ‏ : ‎ Mit Pr (January 1, 2007)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 586 pages
ISBN-10 ‏ : ‎ 0262072882
ISBN-13 ‏ : ‎ 978-0262072885
Item Weight ‏ : ‎ 3.05 pounds
Dimensions ‏ : ‎ 10.5 x 1.5 x 8.5 inches

Statistical Relational Learning (SRL) is a subfield of machine learning that combines statistical methods with relational databases to model complex, structured data. This approach allows for the incorporation of relational information into the learning process, enabling the modeling of relationships between entities in a dataset.

In the book “Introduction to Statistical Relational Learning” by Lise Getoor and Ben Taskar, readers are introduced to the fundamental concepts and techniques of SRL. The book covers topics such as probabilistic graphical models, relational learning, and probabilistic logic programming, providing a comprehensive overview of the field.

One of the key strengths of SRL is its ability to handle data with complex relational structures, such as social networks, knowledge graphs, and biological networks. By capturing the dependencies between entities in a dataset, SRL can provide more accurate and interpretable models compared to traditional machine learning approaches.

Overall, “Introduction to Statistical Relational Learning” is a valuable resource for researchers, practitioners, and students looking to delve into the exciting world of SRL. Whether you are interested in exploring the theoretical foundations of the field or applying SRL techniques to real-world problems, this book offers a solid foundation for understanding and implementing statistical relational learning algorithms.
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