|
|
|
|
| LEADER |
04023nam a22006015i 4500 |
| 001 |
978-3-540-68856-3 |
| 003 |
DE-He213 |
| 005 |
20210615183624.0 |
| 007 |
cr nn 008mamaa |
| 008 |
100301s2008 gw | s |||| 0|eng d |
| 020 |
|
|
|a 9783540688563
|9 978-3-540-68856-3
|
| 024 |
7 |
|
|a 10.1007/978-3-540-68856-3
|2 doi
|
| 050 |
|
4 |
|a QA76.758
|
| 072 |
|
7 |
|a UMZ
|2 bicssc
|
| 072 |
|
7 |
|a COM051230
|2 bisacsh
|
| 072 |
|
7 |
|a UMZ
|2 thema
|
| 072 |
|
7 |
|a UL
|2 thema
|
| 082 |
0 |
4 |
|a 005.1
|2 23
|
| 100 |
1 |
|
|a De Raedt, Luc.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
|
| 245 |
1 |
0 |
|a Logical and Relational Learning
|h [electronic resource] /
|c by Luc De Raedt.
|
| 250 |
|
|
|a 1st ed. 2008.
|
| 264 |
|
1 |
|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2008.
|
| 300 |
|
|
|a XV, 387 p.
|b online resource.
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 347 |
|
|
|a text file
|b PDF
|2 rda
|
| 490 |
1 |
|
|a Cognitive Technologies,
|x 1611-2482
|
| 505 |
0 |
|
|a An Introduction to Logic -- An Introduction to Learning and Search -- Representations for Mining and Learning -- Generality and Logical Entailment -- The Upgrading Story -- Inducing Theories -- Probabilistic Logic Learning -- Kernels and Distances for Structured Data -- Computational Aspects of Logical and Relational Learning -- Lessons Learned.
|
| 520 |
|
|
|a This textbook covers logical and relational learning in depth, and hence provides an introduction to inductive logic programming (ILP), multirelational data mining (MRDM) and (statistical) relational learning (SRL). These subfields of data mining and machine learning are concerned with the analysis of complex and structured data sets that arise in numerous applications, such as bio- and chemoinformatics, network analysis, Web mining, natural language processing, within the rich representations offered by relational databases and computational logic. The author introduces the machine learning and representational foundations of the field and explains some important techniques in detail by using some of the classic case studies centered around well-known logical and relational systems. The book is suitable for use in graduate courses and should be of interest to graduate students and researchers in computer science, databases and artificial intelligence, as well as practitioners of data mining and machine learning. It contains numerous figures and exercises, and slides are available for many chapters.
|
| 650 |
|
0 |
|a Software engineering.
|
| 650 |
|
0 |
|a Artificial intelligence.
|
| 650 |
|
0 |
|a Data mining.
|
| 650 |
|
0 |
|a Database management.
|
| 650 |
|
0 |
|a Information storage and retrieval.
|
| 650 |
|
0 |
|a Application software.
|
| 650 |
1 |
4 |
|a Software Engineering/Programming and Operating Systems.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I14002
|
| 650 |
2 |
4 |
|a Artificial Intelligence.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I21000
|
| 650 |
2 |
4 |
|a Data Mining and Knowledge Discovery.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I18030
|
| 650 |
2 |
4 |
|a Database Management.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I18024
|
| 650 |
2 |
4 |
|a Information Storage and Retrieval.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I18032
|
| 650 |
2 |
4 |
|a Information Systems Applications (incl. Internet).
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I18040
|
| 710 |
2 |
|
|a SpringerLink (Online service)
|
| 773 |
0 |
|
|t Springer Nature eBook
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9783540860129
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9783642057489
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9783540200406
|
| 830 |
|
0 |
|a Cognitive Technologies,
|x 1611-2482
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-540-68856-3
|
| 912 |
|
|
|a ZDB-2-SCS
|
| 912 |
|
|
|a ZDB-2-SXCS
|
| 950 |
|
|
|a Computer Science (SpringerNature-11645)
|
| 950 |
|
|
|a Computer Science (R0) (SpringerNature-43710)
|
