Foundations of Rule Learning
Produktdetaljer
From the reviews: "The book presents a comprehensive overview of modern rule learning techniques, providing an introduction to rule learning in machine learning and data mining. ... This complex approach is intended for researchers and developers in the fields of rule learning." (Smaranda Belciug, Zentralblatt MATH, Vol. 1263, 2013) "Rule learning is one of the core technologies in machine learning, but there is a good reason why nobody has previously had the audacity to write a book on it. The topic is large and complicated. There are a great variety of quite different machine learning activities that all use rules, in different ways, for different purposes. ... [This book] provides a clear overview of the field. One secret to its success lies in the development of a clear unifying terminology that is powerful enough to cover the whole field. ... For the first time we have a consolidated detailed summary of the state of the art in rule learning. This book provides an excellent introduction to the field for the uninitiated, and is likely to lift the horizons of many ... [It] makes the full extent of this toolkit widely accessible to both the novice and the initiate, and clearly maps the research landscape, from the field's foundations in the 1970s through to the many diverse frontiers of current research." Geoffrey I. Webb (Monash University)
Rules – the clearest, most explored and best understood form of knowledge representation – are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning.
The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.