found in biology, finance, the humanities, management sciences,
medicine, physics and similar fields.
For many problems in these fields, there are no conventional ways to
mathematically or analytically solve them completely at low cost. On
the other hand, nature already solved many optimization problems
efficiently. Computational intelligence attempts to mimic
nature-inspired problem-solving strategies and methods.
These strategies can be used to study, model and analyze complex
systems such that it becomes feasible to handle them. Key areas of
computational intelligence are artificial neural networks,
evolutionary computation and fuzzy systems.
As only a few researchers in that field, Rudolf Kruse has contributed
in many important ways to the understanding, modeling and application
of computational intelligence methods. On occasion of his 60th
birthday, a collection of original papers of leading researchers in
the field of computational intelligence has been collected in this
The 17 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers cover topics of state of the art contributions, features and classification, location context, language and semantics, music retrieval, and adaption and HCI.
The aim is to allow a more flexible modeling of phenomena such as uncertainty, imprecision or ignorance.
Such extensions of classical probability theory and statistics are useful in many real-life situations, since uncertainties in data are not only present in the form of randomness --- various types of incomplete or subjective information have to be handled.
About twelve years ago the idea of strengthening the dialogue between the various research communities in the field of data analysis was born and resulted in the International Conference Series on Soft Methods in Probability and Statistics (SMPS).
This book gathers contributions presented at the SMPS'2012 held in Konstanz, Germany. Its aim is to present recent results illustrating new trends in intelligent data analysis.
It gives a comprehensive overview of current research into the fusion of soft computing methods with probability and statistics.
Synergies of both fields might improve intelligent data analysis methods in terms of robustness to noise and applicability to larger datasets, while being able to efficiently obtain understandable solutions of real-world problems.
Soft computing focuses on obtaining working solutions quickly, accepting approximations and unconventional approaches. Its strength lies in its flexibility to create models that suit the needs arising in applications. In addition, it emphasizes the need for intuitive and interpretable models, which are tolerant to imprecision and uncertainty.
Statistics is more rigorous and focuses on establishing objective conclusions based on experimental data by analyzing the possible situations and their (relative) likelihood. It emphasizes the need for mathematical methods and tools to assess solutions and guarantee performance.
Combining the two fields enhances the robustness and generalizability of data analysis methods, while preserving the flexibility to solve real-world problems efficiently and intuitively.
This clearly-structured, classroom-tested textbook/reference presents a methodical introduction to the field of CI. Providing an authoritative insight into all that is necessary for the successful application of CI methods, the book describes fundamental concepts and their practical implementations, and explains the theoretical background underpinning proposed solutions to common problems. Only a basic knowledge of mathematics is required.
Topics and features: provides electronic supplementary material at an associated website, including module descriptions, lecture slides, exercises with solutions, and software tools; contains numerous examples and definitions throughout the text; presents self-contained discussions on artificial neural networks, evolutionary algorithms, fuzzy systems and Bayesian networks; covers the latest approaches, including ant colony optimization and probabilistic graphical models; written by a team of highly-regarded experts in CI, with extensive experience in both academia and industry.
Students of computer science will find the text a must-read reference for courses on artificial intelligence and intelligent systems. The book is also an ideal self-study resource for researchers and practitioners involved in all areas of CI.
This book is the first one entirely devoted to Web usage mining. It originates from the WEBKDD'99 Workshop held during the 1999 KDD Conference. The ten revised full papers presented together with an introductory survey by the volume editors documents the state of the art in this exciting new area. The book presents topical sections on Modeling the User, Discovering Rules and Patterns of Navigation, and Measuring interestingness in Web Usage Mining.