Spatially Explicit Hyperparameter Optimization For Neural Networks
Download Spatially Explicit Hyperparameter Optimization For Neural Networks PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Spatially Explicit Hyperparameter Optimization For Neural Networks book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Spatially Explicit Hyperparameter Optimization for Neural Networks
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.
Landslide: Susceptibility, Risk Assessment and Sustainability
Author: Gopal Krishna Panda
language: en
Publisher: Springer Nature
Release Date: 2024-05-14
The book illustrates a geospatial and geostatistical approach to data analysis, modeling, risk assessment, and visualization, as well as landslide hazard management in the hilly region. This book investigates cutting-edge methodologies based on open source software and R statistical programming and modeling in current decision-making procedures, with a particular emphasis on recent advances in data mining techniques and robust modeling in torrential rainfall and earthquake induced landslide hazard.
AI 2023: Advances in Artificial Intelligence
This two-volume set LNAI 14471-14472 constitutes the refereed proceedings of the 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, held in Brisbane, QLD, Australia during November 28 – December 1, 2023. The 23 full papers presented together with 59 short papers were carefully reviewed and selected from 213 submissions. They are organized in the following topics: computer vision; deep learning; machine learning and data mining; optimization; medical AI; knowledge representation and NLP; explainable AI; reinforcement learning; and genetic algorithm.