In modern applications of machine learning, predicting a single class label is often not enough. Instead we want to predict a large number of variables that depend on each other, such as a class label for every word in a document or for every region in an image. This structured prediction problem is significantly harder than the simple classification problem because we want to learn how the different labels depend on each other. Conditional random fields provide a powerful solution to this problem. They combine the advantages of classification and graphical modeling as they join the ability of graphical models to compactly model multivariate data with the ability of classification methods to perform prediction using large sets of input features. In the past ten years, there has been an explosion of interest in CRFs with applications as diverse as natural language processing, computer vision, and bioinformatics. An Introduction to Conditional Random Fields provides a comprehensive tutorial aimed at application-oriented practitioners seeking to apply CRFs. This survey does not assume previous knowledge of graphical modeling, and so is intended to be useful to practitioners in a wide variety of fields. It includes discussion of feature construction, inference, and parameter estimation in CRFs. Additionally, the monograph also includes sections on practical "tips of the trade" for CRFs that are difficult to find in the published literature.

Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. 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.