doorgaan naar inhoud
Relational knowledge discovery Voorbeeldweergave van dit item
SluitenVoorbeeldweergave van dit item
Bezig met controle...

Relational knowledge discovery

Auteur: M E Müller
Uitgever: New York : Cambridge University Press, 2012.
Serie: Lecture notes on machine learning.
Editie/Formaat:   eBoek : Document : EngelsAlle edities en materiaalsoorten bekijken.
Database:WorldCat
Samenvatting:
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  Meer lezen...
Beoordeling:

(nog niet beoordeeld) 0 met beoordelingen - U bent de eerste

Onderwerpen
Meer in deze trant

 

Zoeken naar een online exemplaar

Zoeken naar een in de bibliotheek beschikbaar exemplaar

&AllPage.SpinnerRetrieving; Bibliotheken met dit item worden gezocht…

Details

Genre/Vorm: Electronic books
Aanvullende fysieke materiaalsoort: Print version:
Müller, M.E. (Martin E.), 1970-
Relational knowledge discovery.
New York : Cambridge University Press, 2012
(DLC) 2011049968
Genre: Document, Internetbron
Soort document: Internetbron, Computerbestand
Alle auteurs / medewerkers: M E Müller
ISBN: 9781139518185 1139518186 9781139047869 1139047868 1280773812 9781280773815 9781139516334 1139516337
OCLC-nummer: 796214849
Beschrijving: 1 online resource.
Inhoud: Cover; Relational Knowledge Discovery; Title; Copyright; Contents; About this book; What it is about; How it is organised; Thanks to:; Chapter 1: Introduction; 1.1 Motivation; 1.1.1 Different kinds of learning; 1.1.2 Applications; 1.2 Related disciplines; 1.2.1 Codes and compression; 1.2.2 Information theory; 1.2.3 Minimum description length; 1.2.4 Kolmogorov complexity; 1.2.5 Probability theory; Conclusion; Chapter 2: Relational knowledge; 2.1 Objects and their attributes; 2.1.1 Collections of things: sets; 2.1.2 Properties of things: relations; 2.1.3 Special properties of relations.
Serietitel: Lecture notes on machine learning.
Verantwoordelijkheid: M.E. Müller.

Fragment:

Introductory textbook presenting relational methods in machine learning.  Meer lezen...

Beoordelingen

Beoordelingen door gebruikers
Beoordelingen van GoodReads worden opgehaald...
Bezig met opvragen DOGObooks-reviews...

Tags

U bent de eerste.

Vergelijkbare items

Bevestig deze aanvraag

Misschien heeft u dit item reeds aangevraagd. Selecteer a.u.b. Ok als u toch wilt doorgaan met deze aanvraag.

Gekoppelde data


<http://www.worldcat.org/oclc/796214849>
library:oclcnum"796214849"
library:placeOfPublication
library:placeOfPublication
owl:sameAs<info:oclcnum/796214849>
rdf:typeschema:Book
rdfs:seeAlso
schema:about
schema:about
schema:about
rdf:typeschema:Intangible
schema:name"COMPUTERS--Intelligence (AI) & Semantics."
schema:about
schema:about
schema:about
schema:about
schema:about
rdf:typeschema:Intangible
schema:name"COMPUTERS--Enterprise Applications--Business Intelligence Tools."
schema:about
schema:author
schema:bookFormatschema:EBook
schema:datePublished"2012"
schema:description"Cover; Relational Knowledge Discovery; Title; Copyright; Contents; About this book; What it is about; How it is organised; Thanks to:; Chapter 1: Introduction; 1.1 Motivation; 1.1.1 Different kinds of learning; 1.1.2 Applications; 1.2 Related disciplines; 1.2.1 Codes and compression; 1.2.2 Information theory; 1.2.3 Minimum description length; 1.2.4 Kolmogorov complexity; 1.2.5 Probability theory; Conclusion; Chapter 2: Relational knowledge; 2.1 Objects and their attributes; 2.1.1 Collections of things: sets; 2.1.2 Properties of things: relations; 2.1.3 Special properties of relations."
schema:description"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."
schema:exampleOfWork<http://worldcat.org/entity/work/id/1075036730>
schema:inLanguage"en"
schema:name"Relational knowledge discovery"
schema:publisher
rdf:typeschema:Organization
schema:name"Cambridge University Press"
schema:url<http://public.eblib.com/EBLPublic/PublicView.do?ptiID=944687>
schema:url<http://www.myilibrary.com?id=368458>
schema:url<http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=458582>
schema:url<http://dx.doi.org/10.1017/CBO9781139047869>
schema:workExample
schema:workExample
schema:workExample
schema:workExample
schema:workExample
schema:workExample
schema:workExample
schema:workExample

Content-negotiable representations

Venster sluiten

Meld u aan bij WorldCat 

Heeft u geen account? U kunt eenvoudig een nieuwe gratis account aanmaken.