Stock photo: cover may vary
Spatially Explicit Hyperparameter Optimization for Neural Networks Hardback - 2021
by Zheng, Minrui
- New
- Hardback
A$355.64
A$28.66
Delivery to USA
Standard delivery: 7 to 14 days
More delivery options
Standard delivery: 7 to 14 days
Ships from Revaluation Books (Devon, United Kingdom)
Details
- Title Spatially Explicit Hyperparameter Optimization for Neural Networks
- Author Zheng, Minrui
- Binding Hardback
- Condition New
- Pages 108
- Volumes 1
- Language ENG
- Publisher Springer-Nature New York Inc
- Publication date 2021
- Illustrated Yes
- Features Illustrated
- Bookseller's Inventory # x-9811653984
- ISBN 9789811653988 / 9811653984
- Weight 0.8 lbs (0.36 kg)
- Dimensions 9.21 x 6.14 x 0.38 in (23.39 x 15.60 x 0.97 cm)
- Category Computers - General Information
- Quantity available 2
About Revaluation Books Devon, United Kingdom
Biblio member since 2020
General bookseller of both fiction and non-fiction.
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
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.
