Weitere Formate
Hardcover - 9783540751960
80,24 €
Autorenfreundlich Bücher kaufen?!
Beschreibung
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.
Details
| Verlag | Springer Berlin |
| Ersterscheinung | 14. Dezember 2014 |
| Maße | 23.5 cm x 15.5 cm |
| Gewicht | 534 Gramm |
| Format | Softcover |
| ISBN-13 | 9783642430466 |
| Auflage | 2012 |
| Seiten | 334 |
Schlagwörter
Artificial Intelligence, Automated Pattern Recognition, Data Mining, Data Mining and Knowledge Discovery, Künstliche Intelligenz, Mustererkennung, Statistics in Engineering - Physics - Computer Science - Chemistry and Earth Sciences, Theoretische Informatik, Theory of Computation, Wahrscheinlichkeitsrechnung und Statistik, Wissensbasierte Systeme - Expertensysteme
Bekannt durch
