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Learning in Non-Stationary Environments Methods and Applications /
Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelli...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Springer New York : Imprint: Springer,
2012.
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| Edición: | 1st ed. 2012. |
| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- Prologue
- Part I: Dynamic Methods for Unsupervised Learning Problems
- Incremental Statistical Measures
- A Granular Description of Data: A Study in Evolvable Systems
- Incremental Spectral Clustering
- Part II: Dynamic Methods for Supervised Classification Problems
- Semi-Supervised Dynamic Fuzzy K-Nearest Neighbors
- Making Early Predictions of the Accuracy of Machine Learning Classifiers
- Incremental Classifier Fusion and its Applications in Industrial Monotiroing and Diagnostics
- Instance-Based Classification and Regression on Data Streams
- Part III: Dynamic Methods for Supervised Regression Problems
- Flexible Evolving Fuzzy Inference Systems from Data Streams (FLEXFIS++)
- Sequential Adaptive Fuzzy Inference System for Function Approximation Problems
- Interval Approach for Evolving Granular System Modeling
- Part IV: Applications of Learning in Non-Stationary Environments
- Dynamic Learning in Multiple Time-Series in a Non-Stationary Environmenty
- Optimizing Feature Calculation in Adaptive Machine Vision Systems
- On-line Quality Contol with Flexible Evolving Fuzzy Systems
- Identification of a Class of Hybrid Dynamic Systems.