Predicting Human Decision-Making, Rosenfeld, Ariel Kraus, Sarit
Автор: Chen Название: Stochastic Methods for Modeling and Predicting Complex Dynamical Systems ISBN: 3031222482 ISBN-13(EAN): 9783031222481 Издательство: Springer Рейтинг: Цена: 37260.00 T Наличие на складе: Нет в наличии. Описание: This book enables readers to understand, model, and predict complex dynamical systems using new methods with stochastic tools. The author presents a unique combination of qualitative and quantitative modeling skills, novel efficient computational methods, rigorous mathematical theory, as well as physical intuitions and thinking. An emphasis is placed on the balance between computational efficiency and modeling accuracy, providing readers with ideas to build useful models in practice. Successful modeling of complex systems requires a comprehensive use of qualitative and quantitative modeling approaches, novel efficient computational methods, physical intuitions and thinking, as well as rigorous mathematical theories. As such, mathematical tools for understanding, modeling, and predicting complex dynamical systems using various suitable stochastic tools are presented. Both theoretical and numerical approaches are included, allowing readers to choose suitable methods in different practical situations. The author provides practical examples and motivations when introducing various mathematical and stochastic tools and merges mathematics, statistics, information theory, computational science, and data science. In addition, the author discusses how to choose and apply suitable mathematical tools to several disciplines including pure and applied mathematics, physics, engineering, neural science, material science, climate and atmosphere, ocean science, and many others. Readers will not only learn detailed techniques for stochastic modeling and prediction, but will develop their intuition as well. Important topics in modeling and prediction including extreme events, high-dimensional systems, and multiscale features are discussed.
Автор: Ariel Rosenfeld, Sarit Kraus Название: Predicting Human Decision-Making: From Prediction to Action ISBN: 1681732769 ISBN-13(EAN): 9781681732763 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 87780.00 T Наличие на складе: Невозможна поставка. Описание: Human decision-making often transcends our formal models of ""rationality."" Designing intelligent agents that interact proficiently with people necessitates the modeling of human behavior and the prediction of their decisions. In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures—from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well as the most recent and cutting edge advances, providing the reader with a panorama of the challenges and solutions in predicting human decision-making.
Автор: Ariel Rosenfeld, Sarit Kraus Название: Predicting Human Decision-Making: From Prediction to Action ISBN: 1681732742 ISBN-13(EAN): 9781681732749 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 66530.00 T Наличие на складе: Невозможна поставка. Описание: Human decision-making often transcends our formal models of ""rationality."" Designing intelligent agents that interact proficiently with people necessitates the modeling of human behavior and the prediction of their decisions. In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures—from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well as the most recent and cutting edge advances, providing the reader with a panorama of the challenges and solutions in predicting human decision-making.
Автор: Manolopoulos Yannis, Vergoulis Thanasis Название: Predicting the Dynamics of Research Impact ISBN: 303086667X ISBN-13(EAN): 9783030866679 Издательство: Springer Цена: 158380.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides its readers with an introduction to interesting prediction and science dynamics problems in the field of Science of Science.
Автор: Atin Basuchoudhary; James T. Bang; Tinni Sen Название: Machine-learning Techniques in Economics ISBN: 3319690132 ISBN-13(EAN): 9783319690131 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book develops a machine-learning framework for predicting economic growth. While machine learning itself is not a new idea, advances in computing technology combined with a dawning realization of its applicability to economic questions makes it a new tool for economists.
Автор: Stainforth, David (professorial Research Fellow, Principal Research Fellow, London School Of Economics And Political Science) Название: Predicting our climate future ISBN: 0198812930 ISBN-13(EAN): 9780198812937 Издательство: Oxford Academ Рейтинг: Цена: 21120.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Diane J. Cook,Narayanan C. Krishnan Название: Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data ISBN: 111889376X ISBN-13(EAN): 9781118893760 Издательство: Wiley Рейтинг: Цена: 104490.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Defines the notion of an activity model learned from sensor data and presents key algorithms that form the core of the field Activity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data provides an in-depth look at computational approaches to activity learning from sensor data.
Название: Predicting structured data ISBN: 0262528045 ISBN-13(EAN): 9780262528047 Издательство: MIT Press Рейтинг: Цена: 57030.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.
Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional constraints found in structured data, poses one of machine learning's greatest challenges: learning functional dependencies between arbitrary input and output domains. This volume presents and analyzes the state of the art in machine learning algorithms and theory in this novel field. The contributors discuss applications as diverse as machine translation, document markup, computational biology, and information extraction, among others, providing a timely overview of an exciting field.
Contributors Yasemin Altun, Gokhan Bakir, Olivier Bousquet, Sumit Chopra, Corinna Cortes, Hal Daume III, Ofer Dekel, Zoubin Ghahramani, Raia Hadsell, Thomas Hofmann, Fu Jie Huang, Yann LeCun, Tobias Mann, Daniel Marcu, David McAllester, Mehryar Mohri, William Stafford Noble, Fernando Perez-Cruz, Massimiliano Pontil, Marc'Aurelio Ranzato, Juho Rousu, Craig Saunders, Bernhard Scholkopf, Matthias W. Seeger, Shai Shalev-Shwartz, John Shawe-Taylor, Yoram Singer, Alexander J. Smola, Sandor Szedmak, Ben Taskar, Ioannis Tsochantaridis, S.V.N Vishwanathan, Jason Weston
Автор: Sean F. Wu Название: The Helmholtz Equation Least Squares Method ISBN: 1493916394 ISBN-13(EAN): 9781493916399 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book represents the HELS (Helmholtz equation least squares) theory and its applications for visualizing acoustic radiation from an arbitrarily shaped vibrating structure in free or confined space. The first serves as a review of the fundamentals in acoustics and the rest cover five specific topics on the HELS theory.
Автор: Bellomo Название: Predicting Pandemics in a Globally Connected World, Volume 1 ISBN: 3030965619 ISBN-13(EAN): 9783030965617 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This contributed volume investigates several mathematical techniques for the modeling and simulation of viral pandemics, with a special focus on COVID-19. Modeling a pandemic requires an interdisciplinary approach with other fields such as epidemiology, virology, immunology, and biology in general. Spatial dynamics and interactions are also important features to be considered, and a multiscale framework is needed at the level of individuals and the level of virus particles and the immune system. Chapters in this volume address these items, as well as offer perspectives for the future.
Автор: Kampakis Название: Predicting the Unknown ISBN: 1484295048 ISBN-13(EAN): 9781484295045 Издательство: Springer Рейтинг: Цена: 55890.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: As a society, we’re in a constant struggle to control uncertainty and predict the unknown. Quite often, we think of scientific fields and theories as being separate from each other. But a more careful investigation can uncover the common thread that ties many of those together. From ChatGPT, to Amazon’s Alexa, to Apple’s Siri, data science, and computer science have become part of our lives. In the meantime, the demand for data scientists has grown, as the field has been increasingly called the “sexiest profession.” This book attempts to specifically cover this gap in literature between data science, machine learning and artificial intelligence (AI). How was uncertainty approached historically, and how has it evolved since? What schools of thought exist in philosophy, mathematics, and engineering, and what role did they play in the development of data science? It uses the history of data science as a stepping stone to explain what the future might hold. Predicting the Unknown provides the framework that will help you understand where AI is headed, and how to best prepare for the world that’s coming in the next few years, both as a society and within a business. It is not technical and avoids equations or technical explanations, yet is written for the intellectually curious reader, and the technical expert interested in the historical details that can help contextualize how we got here. What You’ll Learn * Explore the bigger picture of data science and see how to best anticipate future changes in that field * Understand machine learning, AI, and data science * Examine data science and AI through engaging historical and human-centric narratives Who is This Book For Business leaders and technology enthusiasts who are trying to understand how to think about data science and AI
Казахстан, 010000 г. Астана, проспект Туран 43/5, НП2 (офис 2) ТОО "Логобук" Тел:+7 707 857-29-98 ,+7(7172) 65-23-70 www.logobook.kz
