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Author: Jim Kunce
Publisher:
ISBN: 9780996889766
Category :
Languages : en
Pages :
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Book Description
A Machine-Learning Approach to Parameter Estimation, the sixth volume of the CAS Monograph Series, is now available for download. In this monograph, CAS Fellows Jim Kunce and Som Chatterjee address the use of machine-learning techniques to solve insurance problems. Their model can use any regression-based machine-learning algorithm to analyze the nonlinear relationships between the parameters of statistical distributions and features that relate to a specific problem. Unlike traditional stratification and segmentation, the authors' machine-learning approach to parameter estimation (MLAPE) learns the underlying parameter groups from the data and uses validation to ensure appropriate predictive powe
Author: Jim Kunce
Publisher:
ISBN: 9780996889766
Category :
Languages : en
Pages :
Get Book Here
Book Description
A Machine-Learning Approach to Parameter Estimation, the sixth volume of the CAS Monograph Series, is now available for download. In this monograph, CAS Fellows Jim Kunce and Som Chatterjee address the use of machine-learning techniques to solve insurance problems. Their model can use any regression-based machine-learning algorithm to analyze the nonlinear relationships between the parameters of statistical distributions and features that relate to a specific problem. Unlike traditional stratification and segmentation, the authors' machine-learning approach to parameter estimation (MLAPE) learns the underlying parameter groups from the data and uses validation to ensure appropriate predictive powe
Author: Ne-Zheng Sun
Publisher: Springer
ISBN: 1493923234
Category : Mathematics
Languages : en
Pages : 638
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Book Description
This three-part book provides a comprehensive and systematic introduction to these challenging topics such as model calibration, parameter estimation, reliability assessment, and data collection design. Part 1 covers the classical inverse problem for parameter estimation in both deterministic and statistical frameworks, Part 2 is dedicated to system identification, hyperparameter estimation, and model dimension reduction, and Part 3 considers how to collect data and construct reliable models for prediction and decision-making. For the first time, topics such as multiscale inversion, stochastic field parameterization, level set method, machine learning, global sensitivity analysis, data assimilation, model uncertainty quantification, robust design, and goal-oriented modeling, are systematically described and summarized in a single book from the perspective of model inversion, and elucidated with numerical examples from environmental and water resources modeling. Readers of this book will not only learn basic concepts and methods for simple parameter estimation, but also get familiar with advanced methods for modeling complex systems. Algorithms for mathematical tools used in this book, such as numerical optimization, automatic differentiation, adaptive parameterization, hierarchical Bayesian, metamodeling, Markov chain Monte Carlo, are covered in details. This book can be used as a reference for graduate and upper level undergraduate students majoring in environmental engineering, hydrology, and geosciences. It also serves as an essential reference book for professionals such as petroleum engineers, mining engineers, chemists, mechanical engineers, biologists, biology and medical engineering, applied mathematicians, and others who perform mathematical modeling.
Author: Dominique Laffly
Publisher: John Wiley & Sons
ISBN: 1119720486
Category : Computers
Languages : en
Pages : 299
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Book Description
This book, presented in three volumes, examines environmental disciplines in relation to major players in contemporary science: Big Data, artificial intelligence and cloud computing. Today, there is a real sense of urgency regarding the evolution of computer technology, the ever-increasing volume of data, threats to our climate and the sustainable development of our planet. As such, we need to reduce technology just as much as we need to bridge the global socio-economic gap between the North and South; between universal free access to data (open data) and free software (open source). In this book, we pay particular attention to certain environmental subjects, in order to enrich our understanding of cloud computing. These subjects are: erosion; urban air pollution and atmospheric pollution in Southeast Asia; melting permafrost (causing the accelerated release of soil organic carbon in the atmosphere); alert systems of environmental hazards (such as forest fires, prospective modeling of socio-spatial practices and land use); and web fountains of geographical data. Finally, this book asks the question: in order to find a pattern in the data, how do we move from a traditional computing model-based world to pure mathematical research? After thorough examination of this topic, we conclude that this goal is both transdisciplinary and achievable.
Author: Tommaso Mansi
Publisher: Academic Press
ISBN: 012817594X
Category : Science
Languages : en
Pages : 274
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Book Description
Artificial Intelligence for Computational Modeling of the Heart presents recent research developments towards streamlined and automatic estimation of the digital twin of a patient's heart by combining computational modeling of heart physiology and artificial intelligence. The book first introduces the major aspects of multi-scale modeling of the heart, along with the compromises needed to achieve subject-specific simulations. Reader will then learn how AI technologies can unlock robust estimations of cardiac anatomy, obtain meta-models for real-time biophysical computations, and estimate model parameters from routine clinical data. Concepts are all illustrated through concrete clinical applications.
Author: Saurabh Prasad
Publisher: Springer Nature
ISBN: 3030386171
Category : Computers
Languages : en
Pages : 464
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Book Description
This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.
Author: Mark R. Shapland
Publisher:
ISBN: 9780996889742
Category : Actuarial science
Languages : en
Pages : 116
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Book Description
Author: Christoph Molnar
Publisher: Lulu.com
ISBN: 0244768528
Category : Computers
Languages : en
Pages : 320
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Book Description
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Author: Zheng Rong Yang
Publisher: World Scientific
ISBN: 9814466786
Category : Computers
Languages : en
Pages : 337
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Book Description
This book covers a wide range of subjects in applying machine learning approaches for bioinformatics projects. The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. Second, it introduces state-of-the-art bioinformatics research methods. The theoretical parts and the practical parts are well integrated for readers to follow the existing procedures in individual research.Unlike most of the bioinformatics books on the market, the content coverage is not limited to just one subject. A broad spectrum of relevant topics in bioinformatics including systematic data mining and computational systems biology researches are brought together in this book, thereby offering an efficient and convenient platform for teaching purposes.An essential reference for both final year undergraduates and graduate students in universities, as well as a comprehensive handbook for new researchers, this book will also serve as a practical guide for software development in relevant bioinformatics projects.
Author: Hariom Tatsat
Publisher: "O'Reilly Media, Inc."
ISBN: 1492073008
Category : Computers
Languages : en
Pages : 426
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Book Description
Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You'll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
Author: Emil Hvitfeldt
Publisher: CRC Press
ISBN: 1000461971
Category : Computers
Languages : en
Pages : 402
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Book Description
Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.
