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Machine Learning Methods in the Environmental Sciences

Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This;

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Machine Learning Methods in the Environmental Sciences

Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This;

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Advances in Machine Learning Research

emerging fields in the industry and one of the most popular fields of research in computational sciences. The roots of machine learning methods can;

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Large-Scale Machine Learning in the Earth Sciences

environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers;

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Applied Directional Statistics

of attention due to demands from disciplines such as life sciences or machine learning, the availability of massive data sets requiring adapted;

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Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough

, autonomous vehicles, environmental policies, life sciences, playing computer games, computational theory, and engineering development. The;

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Advances in Machine Learning Applications in Software Engineering

Machine learning is the study of building computer programs that improve their performance through experience. To meet the challenge;

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Data-Driven Science and Engineering

accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new;

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Introduction to Statistical Machine Learning

learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the;

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Machine Learning and Data Mining Approaches to Climate Science

threat of climate change. There has been a veritable explosion in the amount of data produced by satellites, environmental sensors and climate;

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage;

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Computational Sciences and Artificial Intelligence in Industry

This book is addressed to young researchers and engineers in the fields of Computational Science and Artificial Intelligence, ranging;

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Statistics and Machine Learning Methods for EHR Data

The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning;

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Computational Science - ICCS 2020

for Computer Science Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science;

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Computational Science - ICCS 2020

Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science; Computational Health;

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Computational Science - ICCS 2020

for Computer Science Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science;

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Computational Science - ICCS 2020

for Computer Science Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science;

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Computational Science - ICCS 2020

Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science; Computational Health;

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Computational Science ICCS 2021

Sciences; Machine Learning and Data Assimilation for Dynamical Systems; MeshFree Methods and Radial Basis Functions in Computational Sciences;

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Computational Science ICCS 2021

Sciences; Machine Learning and Data Assimilation for Dynamical Systems; MeshFree Methods and Radial Basis Functions in Computational Sciences;

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First-order and Stochastic Optimization Methods for Machine Learning

learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the;

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First-order and Stochastic Optimization Methods for Machine Learning

learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the;

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Introduction to Machine Learning

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful;

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Introduction to Machine Learning

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful;

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Environmental Science

: social sciences along with chemistry and natural sciences. The former facet explores the environmental economics and policies along with chemical;

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Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing

technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and;

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Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing

promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast;

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