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| Author | : Hendrik Blockeel |
| Publisher | : |
| Total Pages | : 170 |
| Release | : 2006 |
| Genre | : |
| ISBN | : |
| Author | : Hendrik Blockeel |
| Publisher | : |
| Total Pages | : 170 |
| Release | : 2006 |
| Genre | : |
| ISBN | : |
| Author | : Luc De Raedt |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 395 |
| Release | : 2008-09-27 |
| Genre | : Computers |
| ISBN | : 3540688560 |
This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning.
| Author | : Sriraam Natarajan |
| Publisher | : Springer |
| Total Pages | : 79 |
| Release | : 2015-03-03 |
| Genre | : Computers |
| ISBN | : 3319136445 |
This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory to handle uncertainty. It provides an overview of the methods and the key assumptions that allow for adaptation to different models and real world applications. The models are highly attractive due to their compactness and comprehensibility but learning their structure is computationally intensive. To combat this problem, the authors review the use of functional gradients for boosting the structure and the parameters of statistical relational models. The algorithms have been applied successfully in several SRL settings and have been adapted to several real problems from Information extraction in text to medical problems. Including both context and well-tested applications, Boosting Statistical Relational Learning from Benchmarks to Data-Driven Medicine is designed for researchers and professionals in machine learning and data mining. Computer engineers or students interested in statistics, data management, or health informatics will also find this brief a valuable resource.
| Author | : B.L.J. Kaczmarek |
| Publisher | : IOS Press |
| Total Pages | : 128 |
| Release | : 2006-08-25 |
| Genre | : Computers |
| ISBN | : 1607501988 |
With the increased possibilities in modern society for companies and institutions to gather data cheaply and efficiently, the subject of Data Mining has become of increasing importance. This interest has inspired a rapidly maturing research field with developments both on a theoretical, as well as on a practical level with the availability of a range of commercial tools. Unfortunately, the widespread application of this technology has been limited by an important assumption in mainstream Data Mining approaches. This assumption – all data resides, or can be made to reside, in a single table – prevents the use of these Data Mining tools in certain important domains, or requires considerable massaging and altering of the data as a pre-processing step. This limitation has spawned a relatively recent interest in richer Data Mining paradigms that do allow structured data as opposed to the traditional flat representation. This publication goes into the different uses of Data Mining, with Multi-Relational Data Mining (MRDM), the approach to Structured Data Mining, as the main subject of this book.
| Author | : Saso Dzeroski |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 410 |
| Release | : 2013-04-17 |
| Genre | : Computers |
| ISBN | : 3662045990 |
As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The first part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programming; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining. This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.
| Author | : |
| Publisher | : |
| Total Pages | : |
| Release | : 2005 |
| Genre | : |
| ISBN | : |
Multi-relational learning has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. As patterns involve multiple relations, the search space of possible hypotheses becomes intractably complex. Many relational knowledge discovery systems have been developed employing various search strategies, search heuristics and pattern language limitations in order to cope with the complexity of hypothesis space. In this work, we propose a relational concept learning technique, which adopts concept descriptions as associations between the concept and the preconditions to this concept and employs a relational upgrade of association rule mining search heuristic, APRIORI rule, to effectively prune the search space. The proposed system is a hybrid predictive inductive logic system, which utilizes inverse resolution for generalization of concept instances in the presence of background knowledge and refines these general patterns into frequent and strong concept definitions with a modified APRIORI-based specialization operator. Two versions of the system are tested for three real-world learning problems: learning a linearly recursive relation, predicting carcinogenicity of molecules within Predictive Toxicology Evaluation (PTE) challenge and mesh design. Results of the experiments show that the proposed hybrid method is competitive with state-of-the-art systems.
| Author | : Lise Getoor |
| Publisher | : MIT Press |
| Total Pages | : 602 |
| Release | : 2019-09-22 |
| Genre | : Computers |
| ISBN | : 0262538687 |
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.
| Author | : zhensong qian |
| Publisher | : |
| Total Pages | : 104 |
| Release | : 2016 |
| Genre | : |
| ISBN | : |
Which doctors prescribe which drugs to which patients? Who upvotes which answers on what topics on Quora? Who has followed whom on Twitter/Weibo? These relationships are all visible in data, and they all contain a wealth of information that could be extracted to be knowledge/wisdom. Statistical Relational Learning (SRL) is a recent growing field which extends traditional machine learning from single-table to multiple inter-related tables. It aims to provide integrated statistical analysis of heterogeneous and interdependent complex data. In the thesis, I focus on modelling the interactions between different attributes and the link itself for such complex heterogeneous and richly interconnected data. First, I describe the FactorBase system which combines advanced analytics from statistical-relational machine learning (SRL) with database systems. Within FactorBase, all statistical objects are stored as first-class citizens as well as raw data. This new SQL-based framework pushes the multi-relational model discovery into a relational database management system. Secondly, to solve the scalability issue of computing cross-table sufficient statistics, a new Virtual Join algorithm is proposed and implemented in FactorBase. Bayesian networks (BNs) and Dependency Networks (DNs) are two major classes of SRL. Thirdly, I utilize FactorBase to extend the state-of-the-art learning algorithm for BN of generative modelling with link uncertainty. The learned model captures correlations between link types, link features, and attributes of nodes, simultaneously. Finally, a fast hybrid approach is proposed for instance level discriminative learning of DNs with competitive predictive power but substantially better scalability.
| Author | : Luc De Raedt |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 395 |
| Release | : 2008-09-12 |
| Genre | : Computers |
| ISBN | : 3540200401 |
This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning.
