Buch, Englisch, 906 Seiten, Format (B × H): 208 mm x 260 mm, Gewicht: 2157 g
Buch, Englisch, 906 Seiten, Format (B × H): 208 mm x 260 mm, Gewicht: 2157 g
ISBN: 978-0-19-853671-0
Verlag: ACADEMIC
The aim of pattern theory is to create mathematical knowledge representations of complex systems, analyse the mathematical properties of the resulting regular structures, and to apply them to practically occuring patterns in nature and the man-made world. Starting from an algebraic formulation of such representations they are studied in terms of their topological, dynamical and probabilistic aspects. Patterns are expressed through their typical behaviour as well as through their variability around their typical form. Employing the representations (regular structures) algorithms are derived for the understanding, recognition, and restoration of observed patterns. The algorithms are investigated through computer experiments.
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
Weitere Infos & Material
- Part I: Pattern Algebra
- 1: Generators and configurations
- 2: Images and patterns
- Part II: Pattern Topology
- 3: Some topologies on regular structures
- Part III: Pattern Dynamics
- 4: Abstract biological patterns
- 5: Patterns of collective behaviour
- 6: Patterns generated from extremum principles
- Part IV: Metric Pattern Theory
- 7: General principles of MPT
- 8: Pattern synthesis
- 9: First limit problem in MPT
- 10: Second limit problem in MPT
- 11: Mixed limit problem in MPT
- Part V: Pattern Deformations
- 12: Chapter 12: Deformation mechanisms
- Part VI: Pattern Inference
- 13: Ends and means in pattern analysis
- 14: Bayesian pattern inference
- 15: Lattice-based models
- 16: Continuum-based models
- 17: Non-Bayesian pattern inference
- 18: Pattern recognition
- Part VII: Creating Regular Structures
- 19: Creating generators
- 20: Creating acceptor functions and connectors
