Spatially explicit hyperparameter optimization for neural networks
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Neural networks as the commonly used machine learning algorithms, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), have been extensively used in the GIScience domain to explore the nonlinear and complex geographic phenomena.
Neural networks as the commonly used machine learning algorithms, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), have been extensively used in the GIScience domain to explore the nonlinear and complex geographic phenomena. However, there are a few studies that investigate the parameter settings of neural networks in GIScience. Moreover, the model performance of neural networks often depends on the parameter setting for a given dataset. Meanwhile, adjusting the parameter configuration of neural networks will increase the overall running time. Therefore, an automated approach is necessary for addressing these limitations in current studies. This book proposes an automated spatially explicit hyperparameter optimization approach to identify optimal or near-optimal parameter settings for neural networks in the GIScience field. Also, the approach improves the computing performance at both model and computing levels. This book is writtenfor researchers of the GIScience field as well as social science subjects.
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Taal: en
Bindwijze: Hardcover
Oorspronkelijke releasedatum: 19 oktober 2021
Aantal pagina's: 108
Hoofdauteur: Minrui Zheng
Hoofduitgeverij: Springer Verlag, Singapore
Editie: 1st ed. 2021
Product breedte: 155 mm
Product lengte: 235 mm
Studieboek: Ja
Verpakking breedte: 155 mm
Verpakking hoogte: 235 mm
Verpakking lengte: 235 mm
Verpakkingsgewicht: 366 g
EAN: 9789811653988