Brain and Nature-inspired Learning, Computation and Recognition – Ebook PDF Instant Delivery – ISBN(s): 9780128197950,0128197951
Product details:
- ISBN-10 : 0128197951
- ISBN-13 : 978-0128197950
- Author: Licheng Jiao, Ronghua Shang, Fang Liu, Weitong Zhang
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.
Table contents:
1. Introduction
2. The models and structure of neural network
3. Theoretical Basis of Natural Computation
4. Theoretical basis of machine learning
5. Theoretical basis of compressive sensing
6. SAR image
7. POLSAR Image Classification
8. Hyperspectral Image
9. Multiobjective Evolutionary Algorithm (MOEA) based Sparse Clustering
10. MOEA Based Community Detection
11. Evolutionary Computation Based Multiobjective Capacitated Arc Routing Optimizations
12. Multiobjective Optimization Algorithm Based Image Segmentation
13. Graph regularized Feature Selection based on spectral learning and subspace learning
14. Semi-supervised learning based on mixed knowledge information and nuclear norm regularization
15. Fast clustering methods based on learning spectral embedding
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