@book {14505,
	title = {Introduction to Statistical Relational Learning},
	year = {2007},
	month = {2007/11/30/},
	publisher = {MIT Press},
	organization = {MIT Press},
	abstract = {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.},
	keywords = {Business \& Economics / Statistics, Computer algorithms, Computers / Database Management / General, Computers / Logic Design, Computers / Machine Theory, Computers / Programming / Algorithms, Education / Statistics, machine learning, Machine learning - Statistical methods, Machine learning/ Statistical methods, Relational databases},
	isbn = {9780262072885},
	author = {Getoor, Lise}
}
@book {16863,
	title = {Foundations of Multidimensional And Metric Data Structures},
	year = {2006},
	month = {2006/08/08/},
	publisher = {Morgan Kaufmann},
	organization = {Morgan Kaufmann},
	abstract = {The field of multidimensional data structures is large and growing very quickly. Here, for the first time, is a thorough treatment of multidimensional point data, object and image-based representations, intervals and small rectangles, and high-dimensional datasets. The book includes a thorough introduction; a comprehensive survey to spatial and multidimensional data structures and algorithms; and implementation details for the most useful data structures. Along with the hundreds of worked exercises and hundreds of illustrations, the result is an excellent and valuable reference tool for professionals in many areas, including computer graphics, databases, geographic information systems (GIS), game programming, image processing, pattern recognition, solid modeling, similarity retrieval, and VLSI design. Award Winner in 2006 "Best Book" competition in Professional and Scholarly Publishing from the Association of American Publishers. Morgan Kaufmann would like to congratulate Hanan Samet on receiving the UCGIS 2009 Research Award! Read the announcement here: http://www.ucgis.org/summer2009/researchaward.htm * First comprehensive work on multidimensional data structures available, a thorough and authoritative treatment. * An algorithmic rather than mathematical approach, with a liberal use of examples that allows the readers to easily see the possible implementation and use. * Each section includes a large number of exercises and solutions to self-test and confirm the reader{\textquoteright}s understanding and suggest future directions. * Written by a well-known authority in the area of spatial data structures who has made many significant contributions to the field. The author{\textquoteright}s website includes: Spatial Index Demos},
	keywords = {Computer algorithms, Computers / Computer Graphics, Computers / Computer Vision \& Pattern Recognition, Computers / Data Modeling \& Design, Computers / Data Processing, Computers / Database Management / General, Computers / Programming / General, Computers / User Interfaces, Data structures (Computer science), Science / Physics / General},
	isbn = {9780123694461},
	author = {Samet, Hanan}
}
@book {15676,
	title = {Algorithm Engineering and Experiments: 4th International Workshop, Alenex 2002, San Francisco, Ca, Usa, January 4-5. 2002 : Revised Papers},
	year = {2002},
	month = {2002/09/17/},
	publisher = {Springer},
	organization = {Springer},
	abstract = {This book constitutes the thoroughly refereed post-proceedings of the 4th International Workshop on Algorithm Engineering and Experiments, ALENEX 2002, held in San Francisico, CA, USA in January 2002.The 15 revised full papers presented were carefully reviewed and selected from 34 submissions. Among the topics addressed are hewistics for algorithms, combinatorial optimization, searching, graph computation, network optimization, scheduling, computational geometry, sorting, and clustering algorithms.},
	keywords = {Architecture / General, Artificial intelligence, Computer algorithms, Computer algorithms/ Congresses, COMPUTERS, Computers / Computer Graphics, Computers / Computer Science, Computers / Data Modeling \& Design, Computers / Data Processing, Computers / General, Computers / Programming / Algorithms, Electronic books, Mathematics / Discrete Mathematics, Medical / General},
	isbn = {9783540439776},
	author = {Mount, Dave and Stein,Clifford}
}
