přejít na obsah
Boosting : foundations and algorithms Náhled dokumentu
ZavřítNáhled dokumentu
Probíhá kontrola...

Boosting : foundations and algorithms

Autor Robert E Schapire; Yoav Freund
Vydavatel: Cambridge, MA : MIT Press, ©2012.
Edice: Adaptive computation and machine learning.
Vydání/formát:   e-kniha : Document : EnglishZobrazit všechny vydání a formáty
Databáze:WorldCat
Shrnutí:
A remarkably rich theory has evolved around boosting, with connections to a range of topics including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by  Přečíst více...
Hodnocení:

(ještě nehodnoceno) 0 zobrazit recenze - Buďte první.

Předmětová hesla:
Více podobných

 

Najít online exemplář

Odkazy na tento dokument

Vyhledat exemplář v knihovně

&AllPage.SpinnerRetrieving; Vyhledávání knihoven, které vlastní tento dokument...

Detaily

Žánr/forma: Electronic books
Doplňující formát: Print version:
Schapire, Robert E.
Boosting.
Cambridge, MA : MIT Press, c2012
(DLC) 2011038972
(OCoLC)758388404
Typ materiálu: Document, Internetový zdroj
Typ dokumentu: Internet Resource, Computer File
Všichni autoři/tvůrci: Robert E Schapire; Yoav Freund
ISBN: 9780262301183 0262301180
OCLC číslo: 794669892
Popis: 1 online resource (xv, 526 p.) : ill.
Obsahy: Foundations of machine learning --
Using AdaBoost to minimize training error --
Direct bounds on the generalization error --
The margins explanation for boosting's effectiveness --
Game theory, online learning, and boosting --
Loss minimization and generalizations of boosting --
Boosting, convex optimization, and information geometry --
Using confidence-rated weak predictions --
Multiclass classification problems --
Learning to rank --
Attaining the best possible accuracy --
Optimally efficient boosting --
Boosting in continuous time.
Název edice: Adaptive computation and machine learning.
Odpovědnost: Robert E. Schapire and Yoav Freund.

Anotace:

A remarkably rich theory has evolved around boosting, with connections to a range of topics including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. --

Recenze

Recenze redakce

Souhrn od vydavatele

"This excellent book is a mind-stretcher that should be read and reread, even bynonspecialists." -- Computing Reviews "Boosting is, quite simply, one of the best-written books I've read on machine Přečíst více...

 
Recenze vložené uživatelem
Nahrávání recenzí GoodReads...
Přebírání recenzí DOGO books...

Štítky

Buďte první.

Podobné dokumenty

Související předmětová hesla:(4)

Seznamy uživatele s tímto dokumentem (1)

Potvrdit tento požadavek

Tento dokument jste si již vyžádali. Prosím vyberte Ok pokud chcete přesto v žádance pokračovat.

Propojená data


Primary Entity

<http://www.worldcat.org/oclc/794669892> # Boosting foundations and algorithms
    a schema:Book, schema:MediaObject, schema:CreativeWork ;
    library:oclcnum "794669892" ;
    library:placeOfPublication <http://experiment.worldcat.org/entity/work/data/1032229637#Place/cambridge_ma> ; # Cambridge, MA
    library:placeOfPublication <http://id.loc.gov/vocabulary/countries/mau> ;
    schema:about <http://id.worldcat.org/fast/1893023> ; # Boosting (Algorithms)
    schema:about <http://id.worldcat.org/fast/1139041> ; # Supervised learning (Machine learning)
    schema:about <http://experiment.worldcat.org/entity/work/data/1032229637#Topic/computers_enterprise_applications_business_intelligence_tools> ; # COMPUTERS / Enterprise Applications / Business Intelligence Tools
    schema:about <http://dewey.info/class/006.31/e23/> ;
    schema:about <http://experiment.worldcat.org/entity/work/data/1032229637#Topic/computers_intelligence_ai_&_semantics> ; # COMPUTERS / Intelligence (AI) & Semantics
    schema:bookFormat schema:EBook ;
    schema:contributor <http://viaf.org/viaf/63543180> ; # Yoav Freund
    schema:copyrightYear "2012" ;
    schema:creator <http://viaf.org/viaf/46940747> ; # Robert E. Schapire
    schema:datePublished "2012" ;
    schema:description "Foundations of machine learning -- Using AdaBoost to minimize training error -- Direct bounds on the generalization error -- The margins explanation for boosting's effectiveness -- Game theory, online learning, and boosting -- Loss minimization and generalizations of boosting -- Boosting, convex optimization, and information geometry -- Using confidence-rated weak predictions -- Multiclass classification problems -- Learning to rank -- Attaining the best possible accuracy -- Optimally efficient boosting -- Boosting in continuous time."@en ;
    schema:description "A remarkably rich theory has evolved around boosting, with connections to a range of topics including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. --"@en ;
    schema:exampleOfWork <http://worldcat.org/entity/work/id/1032229637> ;
    schema:genre "Electronic books"@en ;
    schema:inLanguage "en" ;
    schema:isPartOf <http://experiment.worldcat.org/entity/work/data/1032229637#Series/adaptive_computation_and_machine_learning> ; # Adaptive computation and machine learning.
    schema:isSimilarTo <http://www.worldcat.org/oclc/758388404> ;
    schema:name "Boosting foundations and algorithms"@en ;
    schema:numberOfPages "526" ;
    schema:productID "794669892" ;
    schema:publication <http://www.worldcat.org/title/-/oclc/794669892#PublicationEvent/cambridge_ma_mit_press_c2012> ;
    schema:publisher <http://experiment.worldcat.org/entity/work/data/1032229637#Agent/mit_press> ; # MIT Press
    schema:url <http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=458478> ;
    schema:url <http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267536> ;
    schema:url <http://mitpress.mit.edu/images/products/books/9780262017183-f30.jpg> ;
    schema:url <http://www.books24x7.com/marc.asp?bookid=73652> ;
    schema:url <http://site.ebrary.com/lib/alltitles/Doc?id=10569012> ;
    schema:url <http://www.myilibrary.com?id=365528> ;
    schema:url <http://site.ebrary.com/id/10569012> ;
    schema:workExample <http://worldcat.org/isbn/9780262301183> ;
    wdrs:describedby <http://www.worldcat.org/title/-/oclc/794669892> ;
    .


Related Entities

<http://experiment.worldcat.org/entity/work/data/1032229637#Series/adaptive_computation_and_machine_learning> # Adaptive computation and machine learning.
    a bgn:PublicationSeries ;
    schema:hasPart <http://www.worldcat.org/oclc/794669892> ; # Boosting foundations and algorithms
    schema:name "Adaptive computation and machine learning." ;
    schema:name "Adaptive computation and machine learning" ;
    .

<http://experiment.worldcat.org/entity/work/data/1032229637#Topic/computers_enterprise_applications_business_intelligence_tools> # COMPUTERS / Enterprise Applications / Business Intelligence Tools
    a schema:Intangible ;
    schema:name "COMPUTERS / Enterprise Applications / Business Intelligence Tools"@en ;
    .

<http://experiment.worldcat.org/entity/work/data/1032229637#Topic/computers_intelligence_ai_&_semantics> # COMPUTERS / Intelligence (AI) & Semantics
    a schema:Intangible ;
    schema:name "COMPUTERS / Intelligence (AI) & Semantics"@en ;
    .

<http://id.worldcat.org/fast/1139041> # Supervised learning (Machine learning)
    a schema:Intangible ;
    schema:name "Supervised learning (Machine learning)"@en ;
    .

<http://id.worldcat.org/fast/1893023> # Boosting (Algorithms)
    a schema:Intangible ;
    schema:name "Boosting (Algorithms)"@en ;
    .

<http://viaf.org/viaf/46940747> # Robert E. Schapire
    a schema:Person ;
    schema:familyName "Schapire" ;
    schema:givenName "Robert E." ;
    schema:name "Robert E. Schapire" ;
    .

<http://viaf.org/viaf/63543180> # Yoav Freund
    a schema:Person ;
    schema:familyName "Freund" ;
    schema:givenName "Yoav" ;
    schema:name "Yoav Freund" ;
    .

<http://worldcat.org/isbn/9780262301183>
    a schema:ProductModel ;
    schema:description "electronic bk." ;
    schema:isbn "0262301180" ;
    schema:isbn "9780262301183" ;
    .

<http://www.worldcat.org/oclc/758388404>
    a schema:CreativeWork ;
    rdfs:label "Boosting." ;
    schema:description "Print version:" ;
    schema:isSimilarTo <http://www.worldcat.org/oclc/794669892> ; # Boosting foundations and algorithms
    .


Content-negotiable representations

Zavřít okno

Prosím přihlaste se do WorldCat 

Nemáte účet? Můžete si jednoduše vytvořit bezplatný účet.