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Pattern recognition /
This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-d...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Burlington, MA ; London :
Academic Press,
�2009.
|
| Edición: | 4th ed. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 1. Introduction
- 2. Classifiers based on Bayes Decision
- 3. Linear Classifiers
- 4. Nonlinear Classifiers
- 5. Feature Selection
- 6. Feature Generation I: Data Transformation and Dimensionality Reduction
- 7. Feature Generation II
- 8. Template Matching
- 9. Context Depedant Clarification
- 10. System Evaultion
- 11. Clustering: Basic Concepts
- 12. Clustering Algorithms: Algorithms L Sequential
- 13. Clustering Algorithms II: Hierarchical
- 14. Clustering Algorithms III: Based on Function Optimization
- 15. Clustering Algorithms IV: Clustering
- 16. Cluster Validity.
- Classifiers based on Bayes Decision Theory
- Linear classifiers
- Nonlinear classifiers
- Feature selection
- Feature generation I : data transformation and dimensionality reduction
- Feature generation II
- Template matching
- Context-dependent classification
- Supervised learning : the epilogue
- Clustering algorithms I : sequential algorithms
- Clustering algorithms II : hierarchial algorithms
- Clustering algorithms III : schemes based on function optimization
- Clustering algorithms IV
- Cluster validity.