000			01558cam a2200181 a 4500
020			_a9780521190213 (hardback)
082	0	0	_a006.31 _bMUL/R
100	1		_aMüller, M. E.
245	1	0	_aRelational knowledge discovery
260			_aCambridge ; _aNew York : _bCambridge University Press, _c2012.
300			_avi, 271 p. : _bill. ;
490	0		_aLecture notes on machine learning
520			_a"What 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"--
650		0	_aComputational learning theory.
650		0	_aMachine learning.
650		0	_aRelational databases.
942			_cBK
999			_c34289 _d34289