Witold Pedrycz Reihenfolge der Bücher (Chronologisch)

Focusing on the fundamentals and technology of fuzzy sets, this book offers a clear introduction to key concepts such as information granules and their processing. It covers recent advances in fuzzy modeling, neurocomputing, and higher-order fuzzy sets, enriched with examples, case studies, and problems linked to artificial intelligence. The balanced approach between theory and application makes it suitable for both academic and industrial audiences, as well as an ideal textbook for graduate and undergraduate students in relevant fields.

This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.

"If you torture the data long enough, Nature will confess," stated Nobel-winning economist Ronald Coase. This remains true, yet achieving this goal is challenging. The phrase "long enough" can often translate to "too long" in many applications, making it impractical. To extract insights from large data sets, one must employ advanced "torturing" tools, and Nature is often reluctant to reveal its secrets. Despite these challenges, readers will discover several effective data mining tools within this insightful book. It addresses a wide range of issues related to data mining methodologies, techniques, and algorithms. The content begins with data understanding and preprocessing, progresses through supervised and unsupervised learning methods, and concludes with model assessment and considerations of data security and privacy. This comprehensive approach to the knowledge discovery process distinguishes the book, making it an essential resource among data mining literature. In today’s world, virtually every aspect of human activity is influenced by the necessity and desire to collect data, leading to significant implications for how we understand and utilize this information.

Data Mining Methods for Knowledge Discovery provides an introduction to the data mining methods that are frequently used in the process of knowledge discovery. This book first elaborates on the fundamentals of each of the data mining rough sets, Bayesian analysis, fuzzy sets, genetic algorithms, machine learning, neural networks, and preprocessing techniques. The book then goes on to thoroughly discuss these methods in the setting of the overall process of knowledge discovery. Numerous illustrative examples and experimental findings are also included. Each chapter comes with an extensive bibliography.Data Mining Methods for Knowledge Discovery is intended for senior undergraduate and graduate students, as well as a broad audience of professionals in computer and information sciences, medical informatics, and business information systems.