| 000 | 02632cam a2200385 i 4500 | ||
|---|---|---|---|
| 999 |
_c7245 _d7245 |
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| 001 | 17116449 | ||
| 005 | 20210427133000.0 | ||
| 008 | 120111s2012 enka b 001 0 eng | ||
| 010 | _a 2011049968 | ||
| 020 | _a9780521190213 (hardback) | ||
| 020 | _a0521190215 (hardback) | ||
| 020 | _a9780521122047 (paperback) | ||
| 020 | _a052112204X (paperback) | ||
| 040 |
_aDLC _cDLC _dYDX _dBDX _dYDXCP _dCDX _dUKMGB _dOCLCO _dYNK _dOCLCO _dPUL _dDLC _erda |
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| 050 | 0 | 0 |
_aQ325.7 _b.M85 2012 |
| 082 | 0 | 0 |
_a006.31 _223 _bM.M.R |
| 100 | 1 |
_aMüller, M. E. _q(Martin E.), _d1970- |
|
| 245 | 1 | 0 |
_aRelational knowledge discovery / _cM.E. Müller. |
| 264 | 1 |
_aCambridge ; _aNew York : _bCambridge University Press, _c2012. |
|
| 300 |
_avi, 271 pages. : _billustrations ; _c26 cm. |
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| 336 |
_2rdacontent _atext |
||
| 337 |
_2rdamedia _aunmediated |
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| 338 |
_2rdacarrier _avolume |
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| 490 | 0 | _aLecture notes on machine learning | |
| 504 | _aIncludes bibliographical references (p. 261-266) and index. | ||
| 505 | 0 | _a 1. Introduction; 2. Relational knowledge; 3. From data to hypotheses; 4. Clustering; 5. Information gain; 6. Knowledge and relations; 7. Rough set theory; 8. Inductive logic learning; 9. Ensemble learning; 10. The logic of knowledge; 11. Indexes and bibliography; Bibliography; Index. | |
| 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. | |
| 856 | 4 | 2 |
_3Cover image _uhttp://assets.cambridge.org/97805211/90213/cover/9780521190213.jpg |
| 942 |
_cBK _2ddc |
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