000			02213nam a22003498i 4500
001			CR9781139047869
003			UkCbUP
005			20200124160304.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			110304s2012||||enk o ||1 0|eng|d
020			_a9781139047869 (ebook)
020			_z9780521190213 (hardback)
020			_z9780521122047 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQ325.7 _b.M85 2012
082	0	0	_a006.3/1 _223
100	1		_aMüller, M. E. _q(Martin E.), _d1970- _eauthor.
245	1	0	_aRelational knowledge discovery / _cM.E. Müller.
264		1	_aCambridge : _bCambridge University Press, _c2012.
300			_a1 online resource (vi, 271 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
520			_aWhat 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.
776	0	8	_iPrint version: _z9780521190213
856	4	0	_uhttps://doi.org/10.1017/CBO9781139047869
999			_c520591 _d520589