Assessing And Improving Prediction And Classification Theory And Algorithms In C
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Assessing And Improving Prediction And Classification
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Author : Timothy Masters
language : en
Publisher: Apress
Release Date : 2017-12-19
Assessing And Improving Prediction And Classification written by Timothy Masters and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-19 with Computers categories.
Assess the quality of your prediction and classification models in ways that accurately reflect their real-world performance, and then improve this performance using state-of-the-art algorithms such as committee-based decision making, resampling the dataset, and boosting. This book presents many important techniques for building powerful, robust models and quantifying their expected behavior when put to work in your application. Considerable attention is given to information theory, especially as it relates to discovering and exploiting relationships between variables employed by your models. This presentation of an often confusing subject avoids advanced mathematics, focusing instead on concepts easily understood by those with modest background in mathematics. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Manyof these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the emphasis is on practical applicability, with all code written in such a way that it can easily be included in any program. What You'll Learn Compute entropy to detect problematic predictors Improve numeric predictions using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothing Carry out classification decisions using Borda counts, MinMax and MaxMin rules, union and intersection rules, logistic regression, selection by local accuracy, maximization of the fuzzy integral, and pairwise coupling Harness information-theoretic techniques to rapidly screen large numbers of candidate predictors, identifying those that are especially promising Use Monte-Carlo permutation methods to assessthe role of good luck in performance results Compute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisions Who This Book is For Anyone who creates prediction or classification models will find a wealth of useful algorithms in this book. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.
Data Mining Algorithms In C
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Author : Timothy Masters
language : en
Publisher: Apress
Release Date : 2017-12-15
Data Mining Algorithms In C written by Timothy Masters and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-15 with Computers categories.
Discover hidden relationships among the variables in your data, and learn how to exploit these relationships. This book presents a collection of data-mining algorithms that are effective in a wide variety of prediction and classification applications. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the focus is on practical applicability, with all code written in such a way that it can easily be included into any program. The Windows-based DATAMINE program lets you experiment with the techniques before incorporating them into your own work. What You'll Learn Use Monte-Carlo permutation tests to provide statistically sound assessments of relationships present in your data Discover how combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the data Work with feature weighting as regularized energy-based learning to rank variables according to their predictive power when there is too little data for traditional methods See how the eigenstructure of a dataset enables clustering of variables into groups that exist only within meaningful subspaces of the data Plot regions of the variable space where there is disagreement between marginal and actual densities, or where contribution to mutual information is high Who This Book Is For Anyone interested in discovering and exploiting relationships among variables. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.
Understanding Machine Learning
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Author : Shai Shalev-Shwartz
language : en
Publisher: Cambridge University Press
Release Date : 2014-05-19
Understanding Machine Learning written by Shai Shalev-Shwartz 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 2014-05-19 with Computers categories.
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Algorithms And Computational Theory For Engineering Applications
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Author : Sripada Rama Sree
language : en
Publisher: Springer Nature
Release Date : 2025-01-24
Algorithms And Computational Theory For Engineering Applications written by Sripada Rama Sree and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-24 with Computers categories.
This book goes deeply into the world of algorithms and computational theory and its astounding influence on numerous engineering areas. The book's carefully chosen content highlights the most recent studies, approaches, and real-world applications that are revolutionising engineering. The book is structured into distinct sections, each of which examines an important topic in computational theory and algorithms. The authors propose cutting-edge optimisation methods that revolutionise the way engineers approach engineering problems by allowing them to solve complicated issues quickly and effectively. The book illustrates the techniques and equipment used in the fields of data science and big data analytics to glean insightful information from enormous databases. Data visualisation, predictive modelling, clustering, and anomaly detection are a few examples of how algorithms are used to find patterns and trends that help engineers make well-informed decisions. Before being physically implemented, complex systems are built, tested, and optimised in the virtual environment thanks to computational modelling and simulation. The book examines numerical techniques, finite element analysis, computational fluid dynamics, and other simulation techniques to highlight how algorithms are changing engineering system design and performance optimisation. The book also delves into the intriguing field of robotics and control systems. The book's readers will learn about the algorithms that advance sensor fusion, intelligent control, path planning, and real-time systems, paving the way for innovations in autonomous driving, industrial automation, and smart cities. Readers will learn more about how algorithms and computational theory are modifying engineering environments, opening up new opportunities, and changing industries by examining the book's chapters. This book is a must-have for anyone looking to keep on top of the intersection of algorithms, computational theory, and engineering applications because of its concentration on practical applications and theoretical breakthroughs.
Machine Learning In Geohazard Risk Prediction And Assessment
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Author : Biswajeet Pradhan
language : en
Publisher: Elsevier
Release Date : 2025-07-01
Machine Learning In Geohazard Risk Prediction And Assessment written by Biswajeet Pradhan and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-01 with Science categories.
Machine Learning in Geohazard Risk Prediction and Assessment: From Microscale Analysis to Regional Mapping presents an overview of the most recent developments in machine learning techniques that have reshaped our understanding of geo-materials and management protocols of geo-risk. The book covers a broad category of research on machine-learning techniques that can be applied, from microscopic modeling to constitutive modeling, to physics-based numerical modeling, to regional susceptibility mapping. This is a good reference for researchers, academicians, graduate and undergraduate students, professionals, and practitioners in the field of geotechnical engineering and applied geology. - Introduces machine-learning techniques in the risk management of geo-hazards, particularly recent developments - Covers a broader category of research and machine-learning techniques that can be applied, from microscopic modeling to constitutive modeling, to physics-based numerical modeling, to regional susceptibility mapping - Contains contributions from top researchers around the world, including authors from the UK, USA, Australia, Austria, China, and India
The Elements Of Statistical Learning
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Author : Trevor Hastie
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
The Elements Of Statistical Learning written by Trevor Hastie and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Mathematics categories.
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.
Proceedings Of Fifth Doctoral Symposium On Computational Intelligence
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Author : Abhishek Swaroop
language : en
Publisher: Springer Nature
Release Date : 2024-10-03
Proceedings Of Fifth Doctoral Symposium On Computational Intelligence written by Abhishek Swaroop and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-03 with Computers categories.
This book features high-quality research papers presented at Fifth Doctoral Symposium on Computational Intelligence (DoSCI 2024), jointly organized by Institute of Engineering & Technology, Lucknow, India, and School of Open Learning, University of Delhi in association with University of Calabria, Italy, on May 10, 2024. This book discusses the topics such as computational intelligence, artificial intelligence, deep learning, evolutionary algorithms, swarm intelligence, fuzzy sets and vague sets, rough set theoretic approaches, quantum-inspired computational intelligence, hybrid computational intelligence, machine learning, computer vision, soft computing, distributed computing, parallel and grid computing, cloud computing, high-performance computing, biomedical computing, and decision support and decision making.
Computational Intelligence In Decision And Control Proceedings Of The 8th International Flins Conference
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Author : Da Ruan
language : en
Publisher: World Scientific
Release Date : 2008-08-08
Computational Intelligence In Decision And Control Proceedings Of The 8th International Flins Conference written by Da Ruan and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-08 with Computers categories.
FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to Computational Intelligence for applied research. The contributions to the eighth edition in the series of FLINS conferences cover state-of-the-art research, development, and technology for computational intelligence systems in general, and for intelligent decision and control in particular.
International Conference On Innovative Computing And Communications
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Author : Aboul Ella Hassanien
language : en
Publisher: Springer Nature
Release Date : 2023-07-31
International Conference On Innovative Computing And Communications written by Aboul Ella Hassanien and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-31 with Technology & Engineering categories.
This book includes high-quality research papers presented at the Sixth International Conference on Innovative Computing and Communication (ICICC 2023), which is held at the Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi, India, on February 17–18, 2023. Introducing the innovative works of scientists, professors, research scholars, students, and industrial experts in the field of computing and communication, the book promotes the transformation of fundamental research into institutional and industrialized research and the conversion of applied exploration into real-time applications.
Multivariate Statistical Machine Learning Methods For Genomic Prediction
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Author : Osval Antonio Montesinos López
language : en
Publisher: Springer Nature
Release Date : 2022-02-14
Multivariate Statistical Machine Learning Methods For Genomic Prediction written by Osval Antonio Montesinos López and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-14 with Technology & Engineering categories.
This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.