Machine Learning for Planetary Science

Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems Utilizes case studies to illustrate how machine learning methods can be employed in practice

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  • Author : Joern Helbert
  • Publisher : Elsevier
  • Pages : 400 pages
  • ISBN : 9780128187210
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKMachine Learning for Planetary Science

Machine Learning for Planetary Science

Machine Learning for Planetary Science
  • Author : Joern Helbert,Mario D'Amore,Michael Aye,Hannah Kerner
  • Publisher : Elsevier
  • Release : 15 March 2021
GET THIS BOOKMachine Learning for Planetary Science

Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice

Machine Learning Techniques for Space Weather

Machine Learning Techniques for Space Weather
  • Author : Enrico Camporeale,Simon Wing,Jay Johnson
  • Publisher : Elsevier
  • Release : 31 May 2018
GET THIS BOOKMachine Learning Techniques for Space Weather

Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms.

Machine Learning and Artificial Intelligence in Geosciences

Machine Learning and Artificial Intelligence in Geosciences
  • Author : Anonim
  • Publisher : Academic Press
  • Release : 25 September 2020
GET THIS BOOKMachine Learning and Artificial Intelligence in Geosciences

Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning techniques to describe fractured media, signal augmentation to improve the generalization of deep neural networks, deep generator priors for Bayesian seismic inversion, as well

Advances in Machine Learning and Data Mining for Astronomy

Advances in Machine Learning and Data Mining for Astronomy
  • Author : Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
  • Publisher : CRC Press
  • Release : 29 March 2012
GET THIS BOOKAdvances in Machine Learning and Data Mining for Astronomy

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines

Earth Observation Open Science and Innovation

Earth Observation Open Science and Innovation
  • Author : Pierre-Philippe Mathieu,Christoph Aubrecht
  • Publisher : Springer
  • Release : 23 January 2018
GET THIS BOOKEarth Observation Open Science and Innovation

This book is published open access under a CC BY 4.0 license. Over the past decades, rapid developments in digital and sensing technologies, such as the Cloud, Web and Internet of Things, have dramatically changed the way we live and work. The digital transformation is revolutionizing our ability to monitor our planet and transforming the way we access, process and exploit Earth Observation data from satellites. This book reviews these megatrends and their implications for the Earth Observation community as well

Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification

Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification
  • Author : Anil Kumar,Priyadarshi Upadhyay,A. Senthil Kumar
  • Publisher : CRC Press
  • Release : 30 August 2020
GET THIS BOOKFuzzy Machine Learning Algorithms for Remote Sensing Image Classification

This book covers the state-of-art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of

Machine Learning for Tomographic Imaging

Machine Learning for Tomographic Imaging
  • Author : Ge Wang,Yi Du Zhang,Xiaojing Ye
  • Publisher : Programme: Iop Expanding Physi
  • Release : 30 December 2019
GET THIS BOOKMachine Learning for Tomographic Imaging

Machine learning represents a paradigm shift in tomographic imaging, and image reconstruction is a new frontier of machine learning. This book will meet the needs of those who want to catch the wave of smart imaging. The book targets graduate students and researchers in the imaging community. Open network software, working datasets, and multimedia will be included. The first of its kind in the emerging field of deep reconstruction and deep imaging, Machine Learning for Tomographic Imaging presents the most

Machine Learning for Decision Makers

Machine Learning for Decision Makers
  • Author : Patanjali Kashyap
  • Publisher : Apress
  • Release : 04 January 2018
GET THIS BOOKMachine Learning for Decision Makers

Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. This

Machine Learning and Biometrics

Machine Learning and Biometrics
  • Author : Jucheng Yang,Dong Sun Park,Sook Yoon,Yarui Chen,Chuanlei Zhang
  • Publisher : BoD – Books on Demand
  • Release : 29 August 2018
GET THIS BOOKMachine Learning and Biometrics

We are entering the era of big data, and machine learning can be used to analyze this deluge of data automatically. Machine learning has been used to solve many interesting and often difficult real-world problems, and the biometrics is one of the leading applications of machine learning. This book introduces some new techniques on biometrics and machine learning, and new proposals of using machine learning techniques for biometrics as well. This book consists of two parts: "Biometrics" and "Machine Learning

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
  • Author : Wojciech Samek,Grégoire Montavon,Andrea Vedaldi,Lars Kai Hansen,Klaus-Robert Müller
  • Publisher : Springer Nature
  • Release : 23 October 2019
GET THIS BOOKExplainable AI: Interpreting, Explaining and Visualizing Deep Learning

The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see

Machine Learning

Machine Learning
  • Author : Hamed Farhadi
  • Publisher : BoD – Books on Demand
  • Release : 19 September 2018
GET THIS BOOKMachine Learning

The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled real-time collection and processing of the generated data across different sections. On the one hand, the internet of things (IoT) enabled by cellular telecommunication industry connects various types of sensors that can collect heterogeneous data. On the other hand, the recent advances in

Machine Learning

Machine Learning
  • Author : Tom M. Mitchell,Jaime G. Carbonell,Ryszard S. Michalski
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKMachine Learning

One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involves the study and development of computational models of learning processes. A major goal of research in this field is to build computers capable of improving their performance with practice and of acquiring knowledge on their own. The intent of this book is to provide a snapshot of this field through a broad. representative set of easily assimilated short papers. As such.

Large-Scale Machine Learning in the Earth Sciences

Large-Scale Machine Learning in the Earth Sciences
  • Author : Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser
  • Publisher : CRC Press
  • Release : 01 August 2017
GET THIS BOOKLarge-Scale Machine Learning in the Earth Sciences

From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire