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BRAIN AND NATURE-INSPIRED LEARNING, COMPUTATION AND RECOGNITION

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9780128197950 ::  BRAIN AND NATURE-INSPIRED LEARNING, COMPUTATION AND RECOGNITION
ISBN:

9780128197950

Publisher:ELSEVIER SCIENCE PUB.B.U.
Edition:
Pages:788
Language:INGLES
P.V.P.: 220 € + 4% IVA = 228,80 €
Dto 5% Save 11,44 €
Final amount iva incl. 217,36 €
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<P><I>BRAIN AND NATURE-INSPIRED LEARNING, COMPUTATION AND RECOGNITION</I> 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. </P> <P>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.</P> <BR><BR><UL><LI>PRESENTS AN INVALUABLE SYSTEMATIC INTRODUCTION TO BRAIN AND NATURE-INSPIRED LEARNING, COMPUTATION AND RECOGNITION</LI><LI>DESCRIBES THE BIOLOGICAL MECHANISMS, MATHEMATICAL ANALYSES AND SCIENTIFIC PRINCIPLES BEHIND BRAIN AND NATURE-INSPIRED LEARNING, CALCULATION AND RECOGNITION</LI><LI>SYSTEMATICALLY ANALYZES NEURAL NETWORKS, NATURAL COMPUTING, MACHINE LEARNING AND COMPRESSION, ALGORITHMS AND APPLICATIONS INSPIRED BY THE BRAIN AND BIOLOGICAL MECHANISMS FOUND IN NATURE</LI><LI>DISCUSSES THE THEORY AND APPLICATION OF ALGORITHMS AND NEURAL NETWORKS, NATURAL COMPUTING, MACHINE LEARNING AND COMPRESSION PERCEPTION</LI></UL>

1. INTRODUCTION<BR>2. THE MODELS AND STRUCTURE OF NEURAL NETWORK<BR>3. THEORETICAL BASIS OF NATURAL COMPUTATION<BR>4. THEORETICAL BASIS OF MACHINE LEARNING<BR>5. THEORETICAL BASIS OF COMPRESSIVE SENSING<BR>6. SAR IMAGE<BR>7. POLSAR IMAGE CLASSIFICATION<BR>8. HYPERSPECTRAL IMAGE<BR>9. MULTIOBJECTIVE EVOLUTIONARY ALGORITHM (MOEA) BASED SPARSE CLUSTERING<BR>10. MOEA BASED COMMUNITY DETECTION<BR>11. EVOLUTIONARY COMPUTATION BASED MULTIOBJECTIVE CAPACITATED ARC ROUTING OPTIMIZATIONS<BR>12. MULTIOBJECTIVE OPTIMIZATION ALGORITHM BASED IMAGE SEGMENTATION<BR>13. GRAPH REGULARIZED FEATURE SELECTION BASED ON SPECTRAL LEARNING AND SUBSPACE LEARNING<BR>14. SEMI-SUPERVISED LEARNING BASED ON MIXED KNOWLEDGE INFORMATION AND NUCLEAR NORM REGULARIZATION<BR>15. FAST CLUSTERING METHODS BASED ON LEARNING SPECTRAL EMBEDDING<BR>16. FAST CLUSTERING METHODS BASED ON AFFINITY PROPAGATION AND DENSITY-WEIGHTED<BR>17. SAR IMAGE PROCESSING BASED ON SIMILARITY MEASURE AND DISCRIMINANT FEATURE LEARNING<BR>18. HYPERSPECTRAL IMAGE PROCESSING BASED ON SPARSE LEARNING AND SPARSE GRAPH<BR>19. NON-CONVEX COMPRESSED SENSING FRAMEWORK BASED ON BLOCK STRATEGY AND OVERCOMPLETE DICTIONARY<BR>20. THE SPARSE REPRESENTATION COMBINED WITH FCM IN COMPRESSED SENSING<BR>21. COMPRESSED SENSING BY COLLABORATIVE RECONSTRUCTION<BR>22. HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON SPECTRAL INFORMATION DIVERGENCE AND SPARSE REPRESENTATION

ELECTRICAL ENGINEERING
ENGINEERING / TECHNOLOGY
ENVIRONMENTAL SCIENCES
PHYSICS
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