A Machine Learning Approach To Parameter Estimation

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A Machine-Learning Approach to Parameter Estimation

Author : Jim Kunce
Publisher :
ISBN 13 : 9780996889766
Total Pages : pages
Book Rating : 4.8/5 (897 download)

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Book Synopsis A Machine-Learning Approach to Parameter Estimation by : Jim Kunce

Download or read book A Machine-Learning Approach to Parameter Estimation written by Jim Kunce and published by . This book was released on 2017-07-07 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Classification, Parameter Estimation and State Estimation

Author : Bangjun Lei
Publisher : John Wiley & Sons
ISBN 13 : 1119152445
Total Pages : 480 pages
Book Rating : 4.1/5 (191 download)

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Book Synopsis Classification, Parameter Estimation and State Estimation by : Bangjun Lei

Download or read book Classification, Parameter Estimation and State Estimation written by Bangjun Lei and published by John Wiley & Sons. This book was released on 2017-03-03 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical introduction to intelligent computer vision theory, design, implementation, and technology The past decade has witnessed epic growth in image processing and intelligent computer vision technology. Advancements in machine learning methods—especially among adaboost varieties and particle filtering methods—have made machine learning in intelligent computer vision more accurate and reliable than ever before. The need for expert coverage of the state of the art in this burgeoning field has never been greater, and this book satisfies that need. Fully updated and extensively revised, this 2nd Edition of the popular guide provides designers, data analysts, researchers and advanced post-graduates with a fundamental yet wholly practical introduction to intelligent computer vision. The authors walk you through the basics of computer vision, past and present, and they explore the more subtle intricacies of intelligent computer vision, with an emphasis on intelligent measurement systems. Using many timely, real-world examples, they explain and vividly demonstrate the latest developments in image and video processing techniques and technologies for machine learning in computer vision systems, including: PRTools5 software for MATLAB—especially the latest representation and generalization software toolbox for PRTools5 Machine learning applications for computer vision, with detailed discussions of contemporary state estimation techniques vs older content of particle filter methods The latest techniques for classification and supervised learning, with an emphasis on Neural Network, Genetic State Estimation and other particle filter and AI state estimation methods All new coverage of the Adaboost and its implementation in PRTools5. A valuable working resource for professionals and an excellent introduction for advanced-level students, this 2nd Edition features a wealth of illustrative examples, ranging from basic techniques to advanced intelligent computer vision system implementations. Additional examples and tutorials, as well as a question and solution forum, can be found on a companion website.

Classification, Parameter Estimation and State Estimation

Author : Ferdinand van der Heijden
Publisher : John Wiley & Sons
ISBN 13 : 0470090146
Total Pages : 440 pages
Book Rating : 4.4/5 (7 download)

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Book Synopsis Classification, Parameter Estimation and State Estimation by : Ferdinand van der Heijden

Download or read book Classification, Parameter Estimation and State Estimation written by Ferdinand van der Heijden and published by John Wiley & Sons. This book was released on 2005-06-10 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: Classification, Parameter Estimation and State Estimation is a practical guide for data analysts and designers of measurement systems and postgraduates students that are interested in advanced measurement systems using MATLAB. 'Prtools' is a powerful MATLAB toolbox for pattern recognition and is written and owned by one of the co-authors, B. Duin of the Delft University of Technology. After an introductory chapter, the book provides the theoretical construction for classification, estimation and state estimation. The book also deals with the skills required to bring the theoretical concepts to practical systems, and how to evaluate these systems. Together with the many examples in the chapters, the book is accompanied by a MATLAB toolbox for pattern recognition and classification. The appendix provides the necessary documentation for this toolbox as well as an overview of the most useful functions from these toolboxes. With its integrated and unified approach to classification, parameter estimation and state estimation, this book is a suitable practical supplement in existing university courses in pattern classification, optimal estimation and data analysis. Covers all contemporary main methods for classification and estimation. Integrated approach to classification, parameter estimation and state estimation Highlights the practical deployment of theoretical issues. Provides a concise and practical approach supported by MATLAB toolbox. Offers exercises at the end of each chapter and numerous worked out examples. PRtools toolbox (MATLAB) and code of worked out examples available from the internet Many examples showing implementations in MATLAB Enables students to practice their skills using a MATLAB environment

New Developments in Parsing Technology

Author : H. Bunt
Publisher : Springer Science & Business Media
ISBN 13 : 9781402022944
Total Pages : 424 pages
Book Rating : 4.0/5 (229 download)

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Book Synopsis New Developments in Parsing Technology by : H. Bunt

Download or read book New Developments in Parsing Technology written by H. Bunt and published by Springer Science & Business Media. This book was released on 2005-02-15 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This volume is of specific interest to researchers, advanced undergraduate students, graduate students, and teachers in the following areas: Computational Linguistics, Artificial Intelligence, Computer Science, Language Engineering, Information Science, and Cognitive Science. It will also be of interest to designers, developers, and advanced users of natural language processing software and systems, including applications such as machine translation, information extraction, spoken dialogue, multimodal human-computer interaction, text mining, and semantic web technology."--Jacket.

Mathematics for Machine Learning

Author : Marc Peter Deisenroth
Publisher : Cambridge University Press
ISBN 13 : 1108569323
Total Pages : 392 pages
Book Rating : 4.1/5 (85 download)

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Book Synopsis Mathematics for Machine Learning by : Marc Peter Deisenroth

Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Hyperspectral Image Analysis

Author : Saurabh Prasad
Publisher : Springer Nature
ISBN 13 : 3030386171
Total Pages : 464 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Hyperspectral Image Analysis by : Saurabh Prasad

Download or read book Hyperspectral Image Analysis written by Saurabh Prasad and published by Springer Nature. This book was released on 2020-04-27 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Statistical Reinforcement Learning

Author : Masashi Sugiyama
Publisher : CRC Press
ISBN 13 : 1439856907
Total Pages : 206 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Statistical Reinforcement Learning by : Masashi Sugiyama

Download or read book Statistical Reinforcement Learning written by Masashi Sugiyama and published by CRC Press. This book was released on 2015-03-16 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amo

Interpretable Machine Learning

Author : Christoph Molnar
Publisher : Lulu.com
ISBN 13 : 0244768528
Total Pages : 320 pages
Book Rating : 4.2/5 (447 download)

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Book Synopsis Interpretable Machine Learning by : Christoph Molnar

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning Approaches to Bioinformatics

Author :
Publisher :
ISBN 13 : 9814466786
Total Pages : pages
Book Rating : 4.8/5 (144 download)

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Book Synopsis Machine Learning Approaches to Bioinformatics by :

Download or read book Machine Learning Approaches to Bioinformatics written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Understanding Machine Learning

Author : Shai Shalev-Shwartz
Publisher : Cambridge University Press
ISBN 13 : 1107057132
Total Pages : 415 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Understanding Machine Learning by : Shai Shalev-Shwartz

Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Using the ODP Bootstrap Model

Author : Mark R. Shapland
Publisher :
ISBN 13 : 9780996889742
Total Pages : 116 pages
Book Rating : 4.8/5 (897 download)

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Book Synopsis Using the ODP Bootstrap Model by : Mark R. Shapland

Download or read book Using the ODP Bootstrap Model written by Mark R. Shapland and published by . This book was released on 2016 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Learning and Decision-Making from Rank Data

Author : Lirong Xia
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1681734419
Total Pages : 161 pages
Book Rating : 4.6/5 (817 download)

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Book Synopsis Learning and Decision-Making from Rank Data by : Lirong Xia

Download or read book Learning and Decision-Making from Rank Data written by Lirong Xia and published by Morgan & Claypool Publishers. This book was released on 2019-02-06 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions. Often, such data are represented by rankings. This book surveys some recent progress toward addressing the challenge from the considerations of statistics, computation, and socio-economics. We will cover classical statistical models for rank data, including random utility models, distance-based models, and mixture models. We will discuss and compare classical and state of-the-art algorithms, such as algorithms based on Minorize-Majorization (MM), Expectation-Maximization (EM), Generalized Method-of-Moments (GMM), rank breaking, and tensor decomposition. We will also introduce principled Bayesian preference elicitation frameworks for collecting rank data. Finally, we will examine socio-economic aspects of statistically desirable decision-making mechanisms, such as Bayesian estimators. This book can be useful in three ways: (1) for theoreticians in statistics and machine learning to better understand the considerations and caveats of learning from rank data, compared to learning from other types of data, especially cardinal data; (2) for practitioners to apply algorithms covered by the book for sampling, learning, and aggregation; and (3) as a textbook for graduate students or advanced undergraduate students to learn about the field. This book requires that the reader has basic knowledge in probability, statistics, and algorithms. Knowledge in social choice would also help but is not required.

Predicting Fiscal Crises: A Machine Learning Approach

Author : Klaus-Peter Hellwig
Publisher : International Monetary Fund
ISBN 13 : 1513573586
Total Pages : 66 pages
Book Rating : 4.5/5 (135 download)

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Book Synopsis Predicting Fiscal Crises: A Machine Learning Approach by : Klaus-Peter Hellwig

Download or read book Predicting Fiscal Crises: A Machine Learning Approach written by Klaus-Peter Hellwig and published by International Monetary Fund. This book was released on 2021-05-27 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper I assess the ability of econometric and machine learning techniques to predict fiscal crises out of sample. I show that the econometric approaches used in many policy applications cannot outperform a simple heuristic rule of thumb. Machine learning techniques (elastic net, random forest, gradient boosted trees) deliver significant improvements in accuracy. Performance of machine learning techniques improves further, particularly for developing countries, when I expand the set of potential predictors and make use of algorithmic selection techniques instead of relying on a small set of variables deemed important by the literature. There is considerable agreement across learning algorithms in the set of selected predictors: Results confirm the importance of external sector stock and flow variables found in the literature but also point to demographics and the quality of governance as important predictors of fiscal crises. Fiscal variables appear to have less predictive value, and public debt matters only to the extent that it is owed to external creditors.

Deep Learning and Missing Data in Engineering Systems

Author : Collins Achepsah Leke
Publisher : Springer
ISBN 13 : 3030011801
Total Pages : 179 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Deep Learning and Missing Data in Engineering Systems by : Collins Achepsah Leke

Download or read book Deep Learning and Missing Data in Engineering Systems written by Collins Achepsah Leke and published by Springer. This book was released on 2018-12-13 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation in engineering systems. The missing data estimation processes proposed in the book can be applied in image recognition and reconstruction. To facilitate the imputation of missing data, several artificial intelligence approaches are presented, including: deep autoencoder neural networks; deep denoising autoencoder networks; the bat algorithm; the cuckoo search algorithm; and the firefly algorithm. The hybrid models proposed are used to estimate the missing data in high-dimensional data settings more accurately. Swarm intelligence algorithms are applied to address critical questions such as model selection and model parameter estimation. The authors address feature extraction for the purpose of reconstructing the input data from reduced dimensions by the use of deep autoencoder neural networks. They illustrate new models diagrammatically, report their findings in tables, so as to put their methods on a sound statistical basis. The methods proposed speed up the process of data estimation while preserving known features of the data matrix. This book is a valuable source of information for researchers and practitioners in data science. Advanced undergraduate and postgraduate students studying topics in computational intelligence and big data, can also use the book as a reference for identifying and introducing new research thrusts in missing data estimation.

Model Calibration and Parameter Estimation

Author : Ne-Zheng Sun
Publisher : Springer
ISBN 13 : 1493923234
Total Pages : 621 pages
Book Rating : 4.4/5 (939 download)

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Book Synopsis Model Calibration and Parameter Estimation by : Ne-Zheng Sun

Download or read book Model Calibration and Parameter Estimation written by Ne-Zheng Sun and published by Springer. This book was released on 2015-07-01 with total page 621 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Symmetry-Adapted Machine Learning for Information Security

Author : James (Jong Hyuk) Park
Publisher : MDPI
ISBN 13 : 3039366424
Total Pages : 202 pages
Book Rating : 4.0/5 (393 download)

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Book Synopsis Symmetry-Adapted Machine Learning for Information Security by : James (Jong Hyuk) Park

Download or read book Symmetry-Adapted Machine Learning for Information Security written by James (Jong Hyuk) Park and published by MDPI. This book was released on 2020-12-15 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis.

2020 Winter Simulation Conference (WSC)

Author : IEEE Staff
Publisher :
ISBN 13 : 9781728195001
Total Pages : pages
Book Rating : 4.1/5 (95 download)

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Book Synopsis 2020 Winter Simulation Conference (WSC) by : IEEE Staff

Download or read book 2020 Winter Simulation Conference (WSC) written by IEEE Staff and published by . This book was released on 2020-12-14 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: WSC is the premier international forum for disseminating recent advances in the field of system simulation In addition to a technical program of unsurpassed scope and quality, WSC provides the central meeting for practitioners, researchers, and vendors