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Statements

Subject Item
n2:RIV%2F00216208%3A11320%2F10%3A10080310%21RIV11-GA0-11320___
rdf:type
skos:Concept n15:Vysledek
dcterms:description
In machine learning, one of the main requirements is to build computational models with a high ability to generalize well the extracted knowledge. When training e.g. artificial neural networks, poor generalization is often characterized by over-training. A common method to avoid over-training is the hold-out cross-validation. The basic problem of this method represents, however, appropriate data splitting. In most of the applications, simple random sampling is used. Nevertheless, there are several sophisticated statistical sampling methods suitable for various types of datasets. This paper provides a survey of existing sampling methods applicable to the data splitting problem. Supporting experiments evaluating the benefits of the selected data splitting techniques involve artificial neural networks of the back-propagation type. In machine learning, one of the main requirements is to build computational models with a high ability to generalize well the extracted knowledge. When training e.g. artificial neural networks, poor generalization is often characterized by over-training. A common method to avoid over-training is the hold-out cross-validation. The basic problem of this method represents, however, appropriate data splitting. In most of the applications, simple random sampling is used. Nevertheless, there are several sophisticated statistical sampling methods suitable for various types of datasets. This paper provides a survey of existing sampling methods applicable to the data splitting problem. Supporting experiments evaluating the benefits of the selected data splitting techniques involve artificial neural networks of the back-propagation type.
dcterms:title
Data Splitting Data Splitting
skos:prefLabel
Data Splitting Data Splitting
skos:notation
RIV/00216208:11320/10:10080310!RIV11-GA0-11320___
n4:aktivita
n20:S n20:P
n4:aktivity
P(GD201/09/H057), S
n4:dodaniDat
n12:2011
n4:domaciTvurceVysledku
n7:6605850
n4:druhVysledku
n5:D
n4:duvernostUdaju
n16:S
n4:entitaPredkladatele
n13:predkladatel
n4:idSjednocenehoVysledku
252949
n4:idVysledku
RIV/00216208:11320/10:10080310
n4:jazykVysledku
n21:eng
n4:klicovaSlova
Machine Learning; Hold-out cross-validation; Sampling; Data splitting
n4:klicoveSlovo
n6:Sampling n6:Hold-out%20cross-validation n6:Data%20splitting n6:Machine%20Learning
n4:kontrolniKodProRIV
[2AE33040BE52]
n4:mistoKonaniAkce
Praha
n4:mistoVydani
Praha
n4:nazevZdroje
WDS'10 Proceedings of Contributed Papers: Part I - Mathematics and Computer Sciences
n4:obor
n19:IN
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
1
n4:projekt
n9:GD201%2F09%2FH057
n4:rokUplatneniVysledku
n12:2010
n4:tvurceVysledku
Reitermanová, Zuzana
n4:typAkce
n10:WRD
n4:zahajeniAkce
2010-06-01+02:00
s:numberOfPages
6
n14:hasPublisher
Matfyzpress
n11:isbn
978-80-7378-139-2
n18:organizacniJednotka
11320