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Spatially Explicit Hyperparameter Optimization for Neural Networks (Paperback, 1st ed. 2021)
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Spatially Explicit Hyperparameter Optimization for Neural Networks (Paperback, 1st ed. 2021)
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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. 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
written for researchers of the GIScience field as well as social
science subjects.
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