Focusing on the management of uncertainty, this book offers a fresh perspective that blends theoretical insights with practical applications. It explores a range of issues relevant to finance, management, and marketing, providing readers with tools and strategies to effectively analyze and navigate uncertainty in these fields.
"Data Mining in Agriculture" offers graduate students and researchers a detailed exploration of data mining techniques in agricultural and environmental contexts. It combines theoretical insights with practical examples, featuring graphical representations and Matlab® algorithms. Each chapter includes exercises with solutions to enhance understanding.
An Object-Oriented and UML Approach
Exploring the intersection of software engineering and agriculture, this book delves into innovative techniques that enhance agricultural systems. It covers methodologies for improving efficiency, productivity, and sustainability in farming practices through software solutions. The text features case studies and practical applications, demonstrating how technology can address challenges in the agricultural sector. By integrating software engineering principles, it aims to provide readers with insights into developing effective tools for modern agriculture.
Focusing on the intersection of operations research, computer science, and applied mathematics, this book delves into combinatorial optimization, highlighting its significance in diverse applications such as network design, scheduling, and computational biology. It traces the historical roots of the field to linear programming, noting key advancements like the ellipsoid method and interior point approaches that have revolutionized problem-solving. The text emphasizes the commonality of discrete problems and their connection to linear programming, including the development of approximation algorithms for NP-hard issues.
Combinatorial optimization is a dynamic field at the intersection of operations research, computer science, and applied mathematics, with applications ranging from network design to machine vision and scheduling. It encompasses diverse areas like linear and integer programming, graph theory, and artificial intelligence. The discipline evolved from linear programming, which has significant applications in resource allocation and planning. Key developments, such as the ellipsoid method and interior point approaches, have introduced polynomial-time algorithms that greatly influence combinatorial optimization solutions.
Das Buch setzt die Kindheitserinnerungen der Autorin fort und beschreibt ihre Jugend- und Studentenzeit in der jungen Volksrepublik und wie sie schließlich aus Liebe in die DDR übersiedelte.
This volume covers some of the topics that are related to the rapidly growing field of biomedical informatics. In June 11-12, 2010 a workshop entitled ‘Optimization and Data Analysis in Biomedical Informatics’ was organized at The Fields Institute. Following this event invited contributions were gathered based on the talks presented at the workshop, and additional invited chapters were chosen from world’s leading experts. In this publication, the authors share their expertise in the form of state-of-the-art research and review chapters, bringing together researchers from different disciplines and emphasizing the value of mathematical methods in the areas of clinical sciences. This work is targeted to applied mathematicians, computer scientists, industrial engineers, and clinical scientists who are interested in exploring emerging and fascinating interdisciplinary topics of research. It is designed to further stimulate and enhance fruitful collaborations between scientists from different disciplines.
Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques. Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise. This work encapsulates all the latest applications of robust optimization in data mining. This brief contains an overview of the rapidly growing field of robust data mining research field and presents the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems. This brief will appeal to theoreticians and data miners working in this field.
8th International Conference, Lion 8, Gainesville, FL, USA, February 16-21, 2014. Revised Selected Papers
This book constitutes the thoroughly refereed post-conference proceedings of the 8th International Conference on Learning and Optimization, LION 8, which was held in Gainesville, FL, USA, in February 2014. The 33 contributions presented were carefully reviewed and selected for inclusion in this book. A large variety of topics are covered, such as algorithm configuration; multiobjective optimization; metaheuristics; graphs and networks; logistics and transportation; and biomedical applications.
Current research results in stochastic and deterministic global optimization including single and multiple objectives are explored and presented in this book by leading specialists from various fields. Contributions include applications to multidimensional data visualization, regression, survey calibration, inventory management, timetabling, chemical engineering, energy systems, and competitive facility location. Graduate students, researchers, and scientists in computer science, numerical analysis, optimization, and applied mathematics will be fascinated by the theoretical, computational, and application-oriented aspects of stochastic and deterministic global optimization explored in this book. This volume is dedicated to the 70th birthday of Antanas Žilinskas whois a leading world expert in global optimization. Professor Žilinskas's research has concentrated on studying models for the objective function, the development and implementation of efficient algorithms for global optimization with single and multiple objectives, and application of algorithms for solving real-world practical problems.