Christian Borgelt Livres

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. 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.

Soft computing, as an engineering science, and statistics, as a classical branch of mathematics, emphasize different aspects of data analysis.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.

Over the last forty years there has been a growing interest to extend probability theory and statistics and to allow for more flexible modelling of imprecision, uncertainty, vagueness and ignorance. The fact that in many real-life situations data uncertainty is not only present in the form of randomness (stochastic uncertainty) but also in the form of imprecision/fuzziness is but one point underlining the need for a widening of statistical tools. Most such extensions originate in a „softening“ of classical methods, allowing, in particular, to work with imprecise or vague data, considering imprecise or generalized probabilities and fuzzy events, etc. About ten years ago the idea of establishing a recurrent forum for discussing new trends in the before-mentioned context was born and resulted in the first International Conference on Soft Methods in Probability and Statistics (SMPS) that was held in Warsaw in 2002. In the following years the conference took place in Oviedo (2004), in Bristol (2006) and in Toulouse (2008). In the current edition the conference returns to Oviedo. This edited volume is a collection of papers presented at the SMPS 2010 conference held in Mieres and Oviedo. It gives a comprehensive overview of current research into the fusion of soft methods with probability and statistics.

Eines der spannendsten Themen im Bereich intelligenter Systeme - von namhaften Autoren geschrieben - zum Lernen und Nachschlagen. Das Buch führt in das Thema der Neuronalen Netze ein und weist darüber hinaus den Weg bis zum vollen Verständnis modernster Fuzzy-Systeme. Neuronale Netze sind ein wichtiges Werkzeug in den Bereichen der Datenanalyse und Mustererkennung. Ursprünglich durch das biologische Vorbild inspiriert, wurde eine Vielfalt neuronaler Netze für verschiedenste Anwendungen entwickelt. Ihre Kopplung mit Fuzzy-Systemen führt zu den sogenannten Neuro-Fuzzy-Systemen. Diese weisen die Lernfähigkeit Neuronaler Netze auf und bieten gleichzeitig den Vorteil einer transparenten regelbasierten Struktur. Sie sind daher besonders vorteilhaft für Anwendungsbereiche, in denen verständliche Lösungen aus Daten erzeugt werden müssen.