Brain and Nature-Inspired Learning, Computation and Recognition

Presents the latest on neural networks, natural computing, machine learning and compression, algorithms and applications inspired by the brain

Licheng Jiao (Author), Ronghua Shang (Author), Fang Liu (Author), Weitong Zhang (Author)

9780128197950, Elsevier Science

Paperback, published 21 January 2020

788 pages, 320 illustrations (240 in full color)
23.4 x 19 x 4.8 cm, 1.86 kg

Brain and Nature-Inspired Learning, Computation and Recognition presents a systematic analysis of neural networks, natural computing, machine learning and compression, algorithms and applications inspired by the brain and biological mechanisms found in nature. Sections cover new developments and main applications, algorithms and simulations. Developments in brain and nature-inspired learning have promoted interest in image processing, clustering problems, change detection, control theory and other disciplines. The book discusses the main problems and applications pertaining to bio-inspired computation and recognition, introducing algorithm implementation, model simulation, and practical application of parameter setting.

1. Introduction2. The models and structure of neural network3. Theoretical Basis of Natural Computation4. Theoretical basis of machine learning5. Theoretical basis of compressive sensing6. SAR image7. POLSAR Image Classification8. Hyperspectral Image9. Multiobjective Evolutionary Algorithm (MOEA) based Sparse Clustering10. MOEA Based Community Detection11. Evolutionary Computation Based Multiobjective Capacitated Arc Routing Optimizations12. Multiobjective Optimization Algorithm Based Image Segmentation13. Graph regularized Feature Selection based on spectral learning and subspace learning14. Semi-supervised learning based on mixed knowledge information and nuclear norm regularization15. Fast clustering methods based on learning spectral embedding16. Fast clustering methods based on affinity propagation and density-weighted17. SAR image processing based on similarity measure and discriminant feature learning18. Hyperspectral image processing based on sparse learning and sparse graph19. Non-convex compressed sensing framework based on block strategy and overcomplete dictionary20. The sparse representation combined with FCM in compressed sensing21. Compressed sensing by collaborative reconstruction22. Hyperspectral image classification based on spectral information divergence and sparse representation

Subject Areas: Engineering: general [TBC]