Brain and Nature-Inspired Learning,Computation and Recognition 1st Edition
by Licheng Jiao,Ronghua Shang,Fang Liu,Weitong Zhang
Print Length 页数: 788 pages
Publisher finelybook 出版社: Elsevier; 1 edition (February 4,2020)
Language 语言: English
ISBN-10: 0128197951
ISBN-13: 9780128197950
Book Description
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.
Readers will find solutions to problems in computation and recognition,particularly neural networks,natural computing,machine learning and compressed sensing. This volume offers a comprehensive and well-structured introduction to brain and nature-inspired learning,computation,and recognition.
Copyright
1. Introduction
2. The models and structure of neural networks
3. Theoretical basis of natural computation
4. Theoretical basis of machine learning
5. Theoretical basis of compressive sensing
6. Multiobjective evolutionary algorithm(MOEA)-based sparse clustering
7. MOEA-based community detection
8. Evolutionary computation-based multiobjective capacitated arc routing optimizations
9. Multiobjective optimization algorithm-based image segmentation
10. Graph-regularized feature selection based on spectral learning and subspace learning
11. Semisupervised learning based on nuclear norm regularization
12. Fast clustering methods based on learning spectral embedding
Chapter 13-Fast clustering methods based on affinity propagation and density
weighting
14. SAR image processing based on similarity measures and discriminant feature learningl
15. Hyperspectral image processing based on sparse learning and sparse graph
16. Nonconvex compressed sensing framework based on block strategy and
overcomplete dictionary
17. Sparse representation combined with fuzzy C-means (FCM) in compressed sensing
18. Compressed sensing by collaborative reconstruction
19. Hyperspectral image classification based on spectral information divergence and
sparse representation
20. Neural network-based synthetic aperture radar image processing
21. Neural networks-based polarimetric SAR image classification
22. Deep neural network models for hyperspectral images
Index
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