Selecting Representative Data Sets

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F12%3A00196428" target="_blank" >RIV/68407700:21460/12:00196428 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/12:00380642 RIV/68407700:21240/12:00196428

Výsledek na webu

<a href="http://www.intechopen.com/books/advances-in-data-mining-knowledge-discovery-and-applications/selecting-representative-data-sets" target="_blank" >http://www.intechopen.com/books/advances-in-data-mining-knowledge-discovery-and-applications/selecting-representative-data-sets</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5772/50787" target="_blank" >10.5772/50787</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Selecting Representative Data Sets

Popis výsledku v původním jazyce

Many methods of Data Mining use data sets for setting their parameters, particularly training and testing sets. Setting of parameters corresponds to the learning (training) of the methods. It is e.g. a case of artificial neural networks and other adaptive (iterative) methods. Some of these methods utilize so-called validation set as well. A question that can arise is how to correctly divide or other way preprocess a given data set to these sets, i.e. how select data samples from the original set and place them into the training and testing sets. The chapter focuses on an overview of existing methods that deal with methods of data selection and sampling. A general approach to the problem of data selection to training, testing and eventually validation sets is discussed. To be able to compare individual approaches, model evaluation techniques are discussed as well. Data splitting is one of used approaches to construct training, testing and possibly validation sets, but there are many oth

Název v anglickém jazyce

Selecting Representative Data Sets

Popis výsledku anglicky

Many methods of Data Mining use data sets for setting their parameters, particularly training and testing sets. Setting of parameters corresponds to the learning (training) of the methods. It is e.g. a case of artificial neural networks and other adaptive (iterative) methods. Some of these methods utilize so-called validation set as well. A question that can arise is how to correctly divide or other way preprocess a given data set to these sets, i.e. how select data samples from the original set and place them into the training and testing sets. The chapter focuses on an overview of existing methods that deal with methods of data selection and sampling. A general approach to the problem of data selection to training, testing and eventually validation sets is discussed. To be able to compare individual approaches, model evaluation techniques are discussed as well. Data splitting is one of used approaches to construct training, testing and possibly validation sets, but there are many oth

Druh

C - Kapitola v odborné knize

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/LG12020" target="_blank" >LG12020: Využití pokročilé statistické analýzy a nestatistických separačních metod pro detekování fyzikálních procesů v datech snímaných urychlovači elementárních částic.</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Advances in Data Mining Knowledge Discovery and Applications

ISBN

978-953-51-0748-4

Počet stran výsledku

24

Strana od-do

43-66

Počet stran knihy

418

Název nakladatele

InTech - Open Access Company (InTech Europe)

Místo vydání

Rijeka

Kód UT WoS kapitoly

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