Bayesian Reasoning And Gaussian Processes For Machine Learning Applications
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Bayesian Reasoning And Gaussian Processes For Machine Learning Applications
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Author : Hemachandran K
language : en
Publisher: CRC Press
Release Date : 2022-04-14
Bayesian Reasoning And Gaussian Processes For Machine Learning Applications written by Hemachandran K and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-14 with Business & Economics categories.
This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.
Bayesian Reasoning And Gaussian Processes For Machine Learning Applications
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Author : Hemachandran K
language : en
Publisher: CRC Press
Release Date : 2022-04-19
Bayesian Reasoning And Gaussian Processes For Machine Learning Applications written by Hemachandran K and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-19 with Mathematics categories.
This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.
Bayesian Reasoning And Machine Learning
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Author : David Barber
language : en
Publisher: Cambridge University Press
Release Date : 2012-02-02
Bayesian Reasoning And Machine Learning written by David Barber and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-02 with Computers categories.
A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.
Bayesian Reasoning And Machine Learning
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Author : David Barber
language : en
Publisher: Cambridge University Press
Release Date : 2012-02-02
Bayesian Reasoning And Machine Learning written by David Barber and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-02 with Computers categories.
Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.
Bayesian Reasoning In Data Analysis A Critical Introduction
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Author : Giulio D'agostini
language : en
Publisher: World Scientific
Release Date : 2003-06-13
Bayesian Reasoning In Data Analysis A Critical Introduction written by Giulio D'agostini and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-13 with Mathematics categories.
This book provides a multi-level introduction to Bayesian reasoning (as opposed to “conventional statistics”) and its applications to data analysis. The basic ideas of this “new” approach to the quantification of uncertainty are presented using examples from research and everyday life. Applications covered include: parametric inference; combination of results; treatment of uncertainty due to systematic errors and background; comparison of hypotheses; unfolding of experimental distributions; upper/lower bounds in frontier-type measurements. Approximate methods for routine use are derived and are shown often to coincide — under well-defined assumptions! — with “standard” methods, which can therefore be seen as special cases of the more general Bayesian methods. In dealing with uncertainty in measurements, modern metrological ideas are utilized, including the ISO classification of uncertainty into type A and type B. These are shown to fit well into the Bayesian framework.
Ai 2020 Advances In Artificial Intelligence
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Author : Marcus Gallagher
language : en
Publisher: Springer Nature
Release Date : 2020-11-27
Ai 2020 Advances In Artificial Intelligence written by Marcus Gallagher and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-27 with Computers categories.
This book constitutes the proceedings of the 33rd Australasian Joint Conference on Artificial Intelligence, AI 2020, held in Canberra, ACT, Australia, in November 2020.* The 36 full papers presented in this volume were carefully reviewed and selected from 57 submissions. The paper were organized in topical sections named: applications; evolutionary computation; fairness and ethics; games and swarms; and machine learning. *The conference was held virtually due to the COVID-19 pandemic.
Bayesian Time Series Models
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Author : David Barber
language : en
Publisher: Cambridge University Press
Release Date : 2011-08-11
Bayesian Time Series Models written by David Barber and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-08-11 with Computers categories.
The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.
Gaussian Processes For Machine Learning
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Author : Carl Edward Rasmussen
language : en
Publisher: MIT Press
Release Date : 2005-11-23
Gaussian Processes For Machine Learning written by Carl Edward Rasmussen and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-11-23 with Computers categories.
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Advances In Structural And Multidisciplinary Optimization
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Author : Axel Schumacher
language : en
Publisher: Springer
Release Date : 2017-12-04
Advances In Structural And Multidisciplinary Optimization written by Axel Schumacher and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-04 with Science categories.
The volume includes papers from the WSCMO conference in Braunschweig 2017 presenting research of all aspects of the optimal design of structures as well as multidisciplinary design optimization where the involved disciplines deal with the analysis of solids, fluids or other field problems. Also presented are practical applications of optimization methods and the corresponding software development in all branches of technology.
Research And Development In Intelligent Systems Xxxiii
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Author : Max Bramer
language : en
Publisher: Springer
Release Date : 2016-11-14
Research And Development In Intelligent Systems Xxxiii written by Max Bramer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-14 with Computers categories.
The papers in this volume are the refereed papers presented at AI-2016, the Thirty-sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2016 in both the technical and the application streams. They present new and innovative developments and applications, divided into technical stream sections on Knowledge Discovery and Data Mining, Sentiment Analysis and Recommendation, Machine Learning, AI Techniques, and Natural Language Processing, followed by application stream sections on AI for Medicine and Disability, Legal Liability and Finance, Telecoms and eLearning, and Genetic Algorithms in Action. The volume also includes the text of short papers presented as posters at the conference. This is the thirty-third volume in the Research and Development in Intelligent Systems series, which also incorporates the twenty-fourth volume in the Applications and Innovations in Intelligent Systems series. These series are essential reading for those who wish to keep up to date with developments in this important field.