Mathematical Theories Of Machine Learning Theory And Applications
Download Mathematical Theories Of Machine Learning Theory And Applications full books in PDF, epub, and Kindle. Read online free Mathematical Theories Of Machine Learning Theory And Applications ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Author | : Bin Shi |
Publisher | : Springer |
Total Pages | : 138 |
Release | : 2019-06-12 |
Genre | : Technology & Engineering |
ISBN | : 3030170764 |
This book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing step sizes of gradient descent for escaping strict saddle points in non-convex optimization problems. In the second part, the authors propose algorithms to find local minima in nonconvex optimization and to obtain global minima in some degree from the Newton Second Law without friction. In the third part, the authors study the problem of subspace clustering with noisy and missing data, which is a problem well-motivated by practical applications data subject to stochastic Gaussian noise and/or incomplete data with uniformly missing entries. In the last part, the authors introduce an novel VAR model with Elastic-Net regularization and its equivalent Bayesian model allowing for both a stable sparsity and a group selection.
Author | : Bin Shi |
Publisher | : |
Total Pages | : 133 |
Release | : 2020 |
Genre | : Big data |
ISBN | : 9783030170776 |
This book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing step sizes of gradient descent for escaping strict saddle points in non-convex optimization problems. In the second part, the authors propose algorithms to find local minima in nonconvex optimization and to obtain global minima in some degree from the Newton Second Law without friction. In the third part, the authors study the problem of subspace clustering with noisy and missing data, which is a problem well-motivated by practical applications data subject to stochastic Gaussian noise and/or incomplete data with uniformly missing entries. In the last part, the authors introduce an novel VAR model with Elastic-Net regularization and its equivalent Bayesian model allowing for both a stable sparsity and a group selection. Provides a thorough look into the variety of mathematical theories of machine learning Presented in four parts, allowing for readers to easily navigate the complex theories Includes extensive empirical studies on both the synthetic and real application time series data.
Author | : Chiong, Raymond |
Publisher | : IGI Global |
Total Pages | : 450 |
Release | : 2009-07-31 |
Genre | : Education |
ISBN | : 1605667064 |
Recently, nature has stimulated many successful techniques, algorithms, and computational applications allowing conventionally difficult problems to be solved through novel computing systems. Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science, and Engineering provides the latest findings in nature-inspired algorithms and their applications for breakthroughs in a wide range of disciplinary fields. This defining reference collection contains chapters written by leading researchers and well-known academicians within the field, offering readers a valuable and enriched accumulation of knowledge.
Author | : Paulo S.R. Diniz |
Publisher | : Elsevier |
Total Pages | : 1236 |
Release | : 2023-07-10 |
Genre | : Technology & Engineering |
ISBN | : 032397225X |
Signal Processing and Machine Learning Theory, authored by world-leading experts, reviews the principles, methods and techniques of essential and advanced signal processing theory. These theories and tools are the driving engines of many current and emerging research topics and technologies, such as machine learning, autonomous vehicles, the internet of things, future wireless communications, medical imaging, etc. - Provides quick tutorial reviews of important and emerging topics of research in signal processing-based tools - Presents core principles in signal processing theory and shows their applications - Discusses some emerging signal processing tools applied in machine learning methods - References content on core principles, technologies, algorithms and applications - Includes references to journal articles and other literature on which to build further, more specific, and detailed knowledge
Author | : Taskin Kavzoglu |
Publisher | : CRC Press |
Total Pages | : 444 |
Release | : 2024-09-04 |
Genre | : Technology & Engineering |
ISBN | : 104009905X |
The third edition of the bestselling Classification Methods for Remotely Sensed Data covers current state-of-the-art machine learning algorithms and developments in the analysis of remotely sensed data. This book is thoroughly updated to meet the needs of readers today and provides six new chapters on deep learning, feature extraction and selection, multisource image fusion, hyperparameter optimization, accuracy assessment with model explainability, and object-based image analysis, which is relatively a new paradigm in image processing and classification. It presents new AI-based analysis tools and metrics together with ongoing debates on accuracy assessment strategies and XAI methods. New in this edition: Provides comprehensive background on the theory of deep learning and its application to remote sensing data. Includes a chapter on hyperparameter optimization techniques to guarantee the highest performance in classification applications. Outlines the latest strategies and accuracy measures in accuracy assessment and summarizes accuracy metrics and assessment strategies. Discusses the methods used for explaining inherent structures and weighing the features of ML and AI algorithms that are critical for explaining the robustness of the models. This book is intended for industry professionals, researchers, academics, and graduate students who want a thorough and up-to-date guide to the many and varied techniques of image classification applied in the fields of geography, geospatial and earth sciences, electronic and computer science, environmental engineering, etc.
Author | : Silvio Carta |
Publisher | : John Wiley & Sons |
Total Pages | : 676 |
Release | : 2022-06-07 |
Genre | : Architecture |
ISBN | : 111974962X |
Machine Learning and the City Explore the applications of machine learning and artificial intelligence to the built environment Machine Learning and the City: Applications in Architecture and Urban Design delivers a robust exploration of machine learning (ML) and artificial intelligence (AI) in the context of the built environment. Relevant contributions from leading scholars in their respective fields describe the ideas and techniques that underpin ML and AI, how to begin using ML and AI in urban design, and the likely impact of ML and AI on the future of city design and planning. Each section couples theoretical and technical chapters, authoritative references, and concrete examples and projects that illustrate the efficacy and power of machine learning in urban design. The book also includes: An introduction to the probabilistic logic that underpins machine learning Comprehensive explorations of the applications of machine learning and artificial intelligence to urban environments Practical discussions of the consequences of applied machine learning and the future of urban design Perfect for designers approaching machine learning and AI for the first time, Machine Learning and the City: Applications in Architecture and Urban Design will also earn a place in the libraries of urban planners and engineers involved in urban design.
Author | : Yunqian Ma |
Publisher | : CRC Press |
Total Pages | : 410 |
Release | : 2011-12-20 |
Genre | : Business & Economics |
ISBN | : 1466558873 |
Trained to extract actionable information from large volumes of high-dimensional data, engineers and scientists often have trouble isolating meaningful low-dimensional structures hidden in their high-dimensional observations. Manifold learning, a groundbreaking technique designed to tackle these issues of dimensionality reduction, finds widespread
Author | : Philipp Grohs |
Publisher | : Cambridge University Press |
Total Pages | : 493 |
Release | : 2022-12-31 |
Genre | : Computers |
ISBN | : 1316516784 |
A mathematical introduction to deep learning, written by a group of leading experts in the field.
Author | : Rezvan Pourmansouri |
Publisher | : Cambridge Scholars Publishing |
Total Pages | : 392 |
Release | : 2024-08-09 |
Genre | : Business & Economics |
ISBN | : 1036410358 |
‘Financial Econometrics’ is a comprehensive guide to analyze financial data using econometric techniques. The book covers both basic and advanced topics in time series analysis, regression models, and volatility modeling. It also includes chapters on panel data analysis, financial market microstructure, and applications of machine learning in finance. This book is designed for students, researchers, and finance professionals who seek to enhance their skills in financial data analysis and make more accurate predictions. With real-world examples and practical applications, ‘Financial Econometrics’ provides the essential tools for success in financial analysis.
Author | : Norbert M. Seel |
Publisher | : Springer Science & Business Media |
Total Pages | : 3643 |
Release | : 2011-10-05 |
Genre | : Education |
ISBN | : 1441914277 |
Over the past century, educational psychologists and researchers have posited many theories to explain how individuals learn, i.e. how they acquire, organize and deploy knowledge and skills. The 20th century can be considered the century of psychology on learning and related fields of interest (such as motivation, cognition, metacognition etc.) and it is fascinating to see the various mainstreams of learning, remembered and forgotten over the 20th century and note that basic assumptions of early theories survived several paradigm shifts of psychology and epistemology. Beyond folk psychology and its naïve theories of learning, psychological learning theories can be grouped into some basic categories, such as behaviorist learning theories, connectionist learning theories, cognitive learning theories, constructivist learning theories, and social learning theories. Learning theories are not limited to psychology and related fields of interest but rather we can find the topic of learning in various disciplines, such as philosophy and epistemology, education, information science, biology, and – as a result of the emergence of computer technologies – especially also in the field of computer sciences and artificial intelligence. As a consequence, machine learning struck a chord in the 1980s and became an important field of the learning sciences in general. As the learning sciences became more specialized and complex, the various fields of interest were widely spread and separated from each other; as a consequence, even presently, there is no comprehensive overview of the sciences of learning or the central theoretical concepts and vocabulary on which researchers rely. The Encyclopedia of the Sciences of Learning provides an up-to-date, broad and authoritative coverage of the specific terms mostly used in the sciences of learning and its related fields, including relevant areas of instruction, pedagogy, cognitive sciences, and especially machine learning and knowledge engineering. This modern compendium will be an indispensable source of information for scientists, educators, engineers, and technical staff active in all fields of learning. More specifically, the Encyclopedia provides fast access to the most relevant theoretical terms provides up-to-date, broad and authoritative coverage of the most important theories within the various fields of the learning sciences and adjacent sciences and communication technologies; supplies clear and precise explanations of the theoretical terms, cross-references to related entries and up-to-date references to important research and publications. The Encyclopedia also contains biographical entries of individuals who have substantially contributed to the sciences of learning; the entries are written by a distinguished panel of researchers in the various fields of the learning sciences.