M. E. MüllerCambridge University Press, 6/21/2012EAN 9780521122047, ISBN10: 052112204XPaperback, 278 pages, 24.7 x 17.4 x 1.5 cmLanguage: EnglishWhat is knowledge and how is it represented? This book focuses on the idea of formalising knowledge as relations, interpreting knowledge represented in databases or logic programs as relational data and discovering new knowledge by identifying hidden and defining new relations. After a brief introduction to representational issues, the author develops a relational language for abstract machine learning problems. He then uses this language to discuss traditional methods such as clustering and decision tree induction, before moving onto two previously underestimated topics that are just coming to the fore: rough set data analysis and inductive logic programming. Its clear and precise presentation is ideal for undergraduate computer science students. The book will also interest those who study artificial intelligence or machine learning at the graduate level. Exercises are provided and each concept is introduced using the same example domain, making it easier to compare the individual properties of different approaches.1. Introduction2. Relational knowledge3. From data to hypotheses4. Clustering5. Information gain6. Knowledge and relations7. Rough set theory8. Inductive logic learning9. Ensemble learning10. The logic of knowledge11. Indexes and bibliographyBibliographyIndex.
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