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Spatially explicit hyperparameter op...

Zheng, Minrui.

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Spatially explicit hyperparameter optimization for neural networks

Record Type:	Electronic resources : Monograph/item
Title/Author:	Spatially explicit hyperparameter optimization for neural networks/ by Minrui Zheng.
Author:	Zheng, Minrui.
Published:	Singapore :Springer Singapore : : 2021.,
Description:	xix, 108 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Introduction -- Chapter 2: Literature Review -- Chapter 3: Methodology -- Chapter 4: Study I. Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing -- Chapter 5: Study II. Spatially explicit hyperparameter optimization of neural networks accelerated using high-performance computing -- Chapter 6: Study III. An integration of spatially explicit hyperparameter optimization with convolutional neural networks-based spatial models.
Contained By:	Springer Nature eBook
Subject:	Neural networks (Computer science) -
Online resource:	https://doi.org/10.1007/978-981-16-5399-5
ISBN:	9789811653995

Spatially explicit hyperparameter optimization for neural networks
Zheng, Minrui.

Spatially explicit hyperparameter optimization for neural networks[electronic resource] /by Minrui Zheng. - Singapore :Springer Singapore :2021. - xix, 108 p. :ill., digital ;24 cm.

Chapter 1: Introduction -- Chapter 2: Literature Review -- Chapter 3: Methodology -- Chapter 4: Study I. Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing -- Chapter 5: Study II. Spatially explicit hyperparameter optimization of neural networks accelerated using high-performance computing -- Chapter 6: Study III. An integration of spatially explicit hyperparameter optimization with convolutional neural networks-based spatial models.

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.

ISBN: 9789811653995

Standard No.: 10.1007/978-981-16-5399-5doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87 / .Z44 2021

Dewey Class. No.: 006.32

Spatially explicit hyperparameter optimization for neural networks
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505 0 $a Chapter 1: Introduction -- Chapter 2: Literature Review -- Chapter 3: Methodology -- Chapter 4: Study I. Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing -- Chapter 5: Study II. Spatially explicit hyperparameter optimization of neural networks accelerated using high-performance computing -- Chapter 6: Study III. An integration of spatially explicit hyperparameter optimization with convolutional neural networks-based spatial models.
520 $a 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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