الصفحة 76
الصفحة 76
Machines, Computations, and Universality ; 4th International Conference, MCU 2004, Saint Petersburg, Russia, September 21-24, 2004, Revised Selected Papers
Constitutes the post-proceedings of the 4th International Conference on Machines, Computations, and Universality, MCU 2004, held in St Petersburg, Russia in September 2004. This book covers a variety of foundational aspects in theoretical computer science such as cellular automata, molecular computing, quantum computing, and formal languages
Machine Learning Techniques for Multimedia : Case Studies on Organization and Retrieval
This book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain. Arising from the EU MUSCLE network, a program that drew together multidisciplinary teams with expertise in machine learning, pattern recognition, artificial intelligence, and image, video, text and crossmedia processing, the book first introduces the machine learning principles and techniques that are applied in multimedia data processing and analysis. The second part focuses on multimedia data processing applications, with chapters examining specific machine learning issues in domains .
Machine learning for cyber-physical systems: selected papers from the international conference ML4CPS 2023
Contains selected papers from the international conference ML4CPS – Machine Learning for Cyber-Physical Systems, which was held in Hamburg (Germany), from 29 to 31 March 2023. Cyber-physical systems are adaptive and learning: they analyze their environment and, based on observations, learn patterns, associations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnostics. Machine learning is the key technology for these developments.
Machine Learning for Cyber Security ; 3rd International Conference, ML4CS 2020, Guangzhou, China, October 8–10, 2020, Proceedings, Part II
Constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi’an, China in October 2020. The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning, Malware detection and analysis, Data mining, and Artificial Intelligence.
Machine Learning for Audio, Image and Video Analysis : Theory and Applications
The book is organized in four parts: The first focuses on technical aspects, basic mathematical notions and elementary machine learning techniques. The second provides an extensive survey of most relevant machine learning techniques for media processing, while the third part focuses on applications and shows how techniques are applied in actual problems. The fourth part contains detailed appendices that provide notions about the main mathematical instruments used throughout the text
Machine learning challenges : Evaluating predictive uncertainty, Visual Object Classification, and Recognizing Textual Entailment, 1st Pascal Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, Revised Selected Papers
Constitutes the refereed post-proceedings of the First PASCAL Machine Learning Challenges Workshop, MLCW 2005. 25 papers address three challenges: finding an assessment base on the uncertainty of predictions using classical statistics, Bayesian inference, and statistical learning theory; second, recognizing objects from a number of visual object classes in realistic scenes; third, recognizing textual entailment addresses semantic analysis of language to form a generic framework for applied semantic inference in text understanding.
Machine Learning and Probabilistic Graphical Models for Decision Support Systems
Presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.
Machine Learning and Data Mining in Pattern Recognition ; 4th International Conference, MLDM 2005, Leipzig, Germany, July 9-11, 2005, Proceedings
Today, artificial intelligence deals with large amounts of data and knowledge and finds new information using machine learning and data mining. Machine learning and data mining are irreplaceable subjects and tools for the theory of pattern recognition and in applications of pattern recognition such as bioinformatics and data retrieval. This was the fourth edition of MLDM in Pattern Recognition which is the main event of Technical Committee 17 of the International Association for Pattern Recognition; it started out as a workshop and continued as a conference in 2003. Today, there are many international meetings which are titled “machine learning” and “data mining”, whose topics are text mining, knowledge discovery, and applications. This meeting from the first focused on aspects of machine learning and data mining in pattern recognition problems. We planned to reorganize classical and well-established pattern recognition paradigms from the viewpoints of machine learning and data mining. Though it was a challenging program in the late 1990s, the idea has inspired new starting points in pattern recognition and effects in other areas such as cognitive computer vision.
Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks
Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.
Machine Learning : The Basics
Approaches ML as the computational implementation of the scientific principle. This principle consists of continuously adapting a model of a given data-generating phenomenon by minimizing some form of loss incurred by its predictions. Trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods.
Machine Learning : Modeling Data Locally and Globally
Machine Learning - Modeling Data Locally and Globally presents a novel and unified theory that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the inner nature of machine learning algorithms as either "local learning"or "global learning."This theory not only connects previous machine learning methods, or serves as roadmap in various models, but – more importantly – it also motivates a theory that can learn from data both locally and globally. This would help the researchers gain a deeper insight and comprehensive understanding of the techniques in this field. The book reviews current topics,new theories and applications.
Low-Cost Approaches to Promote Physical and Mental Health : Theory, Research, and Practice
Most physical and mental health professionals will agree that their time, space, and funds are generally in short supply, even under optimal conditions. Their participants (clients or patients), too, will admit to similar deficits of time and patience, even with optimal motivation. Overburdened mental health facilities are trying to cope with limited budgets and overworked and underpaid personnel. Low-Cost Approaches to Promote Physical and Mental Health addresses both sides of this shortfall by offering either self-administered or easily administered verbal and non-verbal interventions designed to promote positive health behaviors while requiring little or no outside funding.
Low Power Methodology Manual : For System-on-Chip Design
"Tools alone aren't enough to reduce dynamic and leakage power in complex chip designs - a well-planned methodology is needed. Following in the footsteps of the successful Reuse Methodology Manual (RMM), authors from ARM and Synopsys have written this Low Power Methodology Manual (LPMM) to describe [such] [a] low-power methodology with a practical, step-by-step approach." "Excellent compendium of low-power techniques and guidelines with balanced content spanning theory and practical implementation. The LPMM is a very welcome addition to the field of low power SoC implementation that has for many years operated in a largely ad-hoc fashion."
Lost Causes in and beyond Physics
Lost Causes in and Beyond Physics deals with a selection of research topics mostly from theoretical physics that have been shown to be a dead-end or continue at least to be highly controversial. This book is written as both an entertainment and serious study and should be accessible to anyone with a background in theoretical physics and mathematics.
Loop Spaces, Characteristic Classes and Geometric Quantization
This book deals with the differential geometry of manifolds, loop spaces, line bundles and groupoids, and the relations of this geometry to mathematical physics. Various developments in mathematical physics (e.g., in knot theory, gauge theory, and topological quantum field theory) have led mathematicians and physicists to search for new geometric structures on manifolds and to seek a synthesis of ideas from geometry, topology and category theory. In this spirit, this book develops the differential geometry associated to the topology and obstruction theory of certain fiber bundles (more precisely, associated to grebes). The theory is a 3-dimensional analog of the familiar Kostant--Weil theory of line bundles. In particular the curvature now becomes a 3-form.
Long Memory in Economics
When applying the statistical theory of long range dependent (LRD) processes to economics, the strong complexity of macroeconomic and financial variables, compared to standard LRD processes, becomes apparent. In order to get a better understanding of the behaviour of some economic variables, the book assembles three different strands of long memory analysis: statistical literature on the properties of, and tests for, LRD processes; mathematical literature on the stochastic processes involved; models from economic theory providing plausible micro foundations for the occurence of long memory in economics. Each chapter of the book will give a comprehensive survey of the state of the art and the directions that future developments are likely to take. Taken as a whole the book provides an overview of LRD processes which is accessible to economists, econometricians and statisticians.
Logical foundations of computer science ; International Symposium, LFCS 2007, New York, NY, USA, June 4-7, 2007, Proceedings
Constitutes the refereed proceedings of the International Symposium on Logical Foundations of Computer Science, LFCS 2007, held in New York, NY, USA in June 2007. The volume presents 36 revised refereed papers that address all current aspects of logic in computer science.
Logical approaches to computational barriers ; 2nd Conference on Computability in Europe, CiE 2006, Swansea, UK, June 30-July 5, 2006, Proceedings
The sources of new ideas and methods include practical developments in areas such as neural networks, quantum computation, natural computation, molecular computation, and computational learning. Applications are everywhere, especially, in algebra, analysis and geometry, or data types and programming. This volume, Logical Approaches to Computational Barriers, is the proce- ings of the second in a series of conferences of CiE that was held at the Depa- ment of Computer Science, Swansea University, 30 June - 5 July, 2006.
Logica Universalis : Towards a General Theory of Logic
Modern logic has been intimately connected with algebra since its origins in figures such as Boole, De Morgan, and Peirce. But while universal algebra is a long recognized field, universal logic has only recently been named as such. This is perhaps because classical logic was until relatively recently taken by many as the "one true logic". But with the proliferation of special purpose non-classical logics in recent years, universal logic is clearly a field whose time has come. This book contains many excellent papers demonstrating the value of this approach.
Logica Universalis : Towards a General Theory of Logic
Signifies the arrival of a new renaissance in logic, a new revival not only of logic, but of the vision of logic as a unifying tool for science as a whole, including mathematics, physics, cosmology, computer science and AI. The book and the vision behind it give logic, conceived as a scientific study of rationality, new unifying power, new perspectives, and new horizons.Universal Logic is not a new logic, but a general theory of logics, considered as mathematical structures. The name was introduced about ten years ago, but the subject is as old as the beginning of modern logic: Alfred Tarski and other Polish logicians such as Adolf Lindenbaum developed a general theory of logics at the end of the 1920s based on consequence operations and logical matrices. The subject was revived after the flowering of thousands of new logics during the last thirty years: there was a need for a systematic theory of logics to put some order in this chaotic multiplicity.
