About: Data augmentation

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Data augmentation in data analysis are techniques used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data. It acts as a regularizer and helps reduce overfitting when training a machine learning model. It is closely related to oversampling in data analysis.

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rdfs:label	Data augmentation (en) 데이터 첨가 (ko)
rdfs:comment	Data augmentation in data analysis are techniques used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data. It acts as a regularizer and helps reduce overfitting when training a machine learning model. It is closely related to oversampling in data analysis. (en) 데이터 첨가(data augmentation) 또는 데이터 증강은 주로 기계 학습을 위해 새로운 데이터를 첨가하거나, 수를 늘리는 기법이다. 과대표집(oversampling)과 관련이 있다. 정칙화 기능을 하여 기계 학습 모델을 훈련할 때 과적합을 줄이는 데에 도움이 된다. (ko)
foaf:depiction
dct:subject	Machine learning
Wikipage page ID	51443362 (xsd:integer)
Wikipage revision ID	1120100399 (xsd:integer)
Link from a Wikipage to another Wikipage	Electroencephalography Electromyography Generative adversarial network GPT-2 Convolutional neural network Machine learning Machine learning Transfer learning Data fusion Data pre-processing Data preparation Oversampling and undersampling in data analysis Regularization (mathematics) Bootstrapping (statistics) Overfitting Variational autoencoder Recurrent Neural Network dbr:Convolutional_Neural_Networks
sameAs	Data augmentation Data augmentation Data augmentation Data augmentation
dbp:wikiPageUsesTemplate	dbt:Data dbt:Main dbt:One_source dbt:Reflist dbt:Short_description dbt:Machine_learning dbt:Differentiable_computing
thumbnail	wiki-commons:Special:FilePath/Synthetic_and_Human_Brainwaves.jpg?width=300
has abstract	Data augmentation in data analysis are techniques used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data. It acts as a regularizer and helps reduce overfitting when training a machine learning model. It is closely related to oversampling in data analysis. (en) 데이터 첨가(data augmentation) 또는 데이터 증강은 주로 기계 학습을 위해 새로운 데이터를 첨가하거나, 수를 늘리는 기법이다. 과대표집(oversampling)과 관련이 있다. 정칙화 기능을 하여 기계 학습 모델을 훈련할 때 과적합을 줄이는 데에 도움이 된다. (ko)
prov:wasDerivedFrom	wikipedia-en:Data_augmentation?oldid=1120100399&ns=0
page length (characters) of wiki page	12014 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Data_augmentation
is Link from a Wikipage to another Wikipage of	Deep learning super sampling Best–worst scaling Generative adversarial network Glossary of artificial intelligence Convolutional neural network MNIST database Machine learning in earth sciences Data cleansing Data preparation Diyi Yang Oversampling and undersampling in data analysis Support vector machine Stable Diffusion Small object detection Variational autoencoder
is foaf:primaryTopic of	wikipedia-en:Data_augmentation

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