Learning in Non-Stationary Environments

1. Prologue
Moamar Sayed-Mouchaweh, Edwin Lughofer

2. Incremental Statistical Measures
Katharina Tschumitschew, Frank Klawonn

3. A Granular Description of Data: A Study in Evolvable Systems
Witold Pedrycz, John Berezowski, Iqbal Jamal

4. Incremental Spectral Clustering
Abdelhamid Bouchachia, Markus Prossegger

5. Semisupervised Dynamic Fuzzy K-Nearest Neighbors
Laurent Hartert, Moamar Sayed-Mouchaweh

6. Making Early Predictions of the Accuracy of Machine Learning Classifiers
James Edward Smith, Muhammad Atif Tahir, Davy Sannen, Hendrik Brussel

7. Incremental Classifier Fusion and Its Applications in Industrial Monitoring and Diagnostics
Davy Sannen, Jean-Michel Papy, Steve Vandenplas, Edwin Lughofer, Hendrik Brussel

8. Instance-Based Classification and Regression on Data Streams
Ammar Shaker, Eyke Hüllermeier

9. Flexible Evolving Fuzzy Inference Systems from Data Streams (FLEXFIS++)
Edwin Lughofer

10. Sequential Adaptive Fuzzy Inference System for Function Approximation Problems
Hai-Jun Rong

11. Interval Approach for Evolving Granular System Modeling
Daniel Leite, Pyramo Costa, Fernando Gomide

12. Dynamic Learning of Multiple Time Series in a Nonstationary Environment
Harya Widiputra, Russel Pears, Nikola Kasabov

13. Optimizing Feature Calculation in Adaptive Machine Vision Systems
Christian Eitzinger, Stefan Thumfart

14. Online Quality Control with Flexible Evolving Fuzzy Systems
Edwin Lughofer, Christian Eitzinger, Carlos Guardiola

15. Identification of a Class of Hybrid Dynamic Systems
Moamar Sayed-Mouchaweh, Nadhir Messai, Omar Ayad, Sofiane Mazeghrane

Nyckelord: Engineering, Computational Intelligence, Data Mining and Knowledge Discovery, Pattern Recognition