Home Spatially Explicit Hyperparameter Optimization for Neural Networks by Minrui Zheng (ISBN: 9789811654015)

Stock photo: cover may vary

Spatially Explicit Hyperparameter Optimization for Neural Networks Paperback - 2022

Name: Spatially Explicit Hyperparameter Optimization for Neural Networks
Price: 134.02 GBP
Availability: InStock
Author: Minrui Zheng
ISBN: 9789811654015

by Minrui Zheng

Add to wish list

Browse more copies for sale ↓

Reader reviews for Spatially Explicit Hyperparameter Optimization for Neural Networks

Write a review for this book

Important Terms and Guidelines

Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
Reviews must be at least 140 characters in length.
Please do not reveal critical plot elements.
This is not a help line. Contact customer support if you need help.

Your review must not include:

Obscenities, discriminatory language, or other insulting language not suitable for public domain
Advertisements, “spam” content, or references to other products, offers or websites.
Email addresses, URLs, phone numbers, physical addresses or other contact information.
Overly critical comments about other reviews or reviewers
Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
Availability, price, or alternative ordering/shipping information

From the publisher

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.

From the rear cover

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.

Details

Title Spatially Explicit Hyperparameter Optimization for Neural Networks
Author Minrui Zheng
Binding Paperback
Pages 108
Volumes 1
Language ENG
Publisher Springer
Publication date 2022-10-20
Illustrated Yes
Features Illustrated
ISBN 9789811654015 / 9811654018
Weight 0.42 lbs (0.19 kg)
Dimensions 9.21 x 6.14 x 0.28 in (23.39 x 15.60 x 0.71 cm)
Category Computers - General Information

About the author

Dr. Minrui Zheng is an Associate Professor in the School of Public Administration and Policy at Renmin University of China. She earned her M.S. in mathematical finance and her Ph.D. from the University of North Carolina at Charlotte. She has published over 10 articles in peer-reviewed journals and book chapters, and is a Member of several professional organizations including the American Association of Geographers and the North American Regional Science Council. Her research and teaching interests focus on GIScience, spatial analysis and modeling, machine learning, high-performance and parallel computing, and land change modeling. Her work focuses on using advanced spatial modeling techniques and high-performance and parallel computing to analyze big data-driven spatial problems.

More Copies for Sale