Natural Computing for Unsupervised Learning
-15%
portes grátis
Natural Computing for Unsupervised Learning
Wong, Ka-Chun; Li, Xiangtao
Springer International Publishing AG
11/2018
273
Dura
Inglês
9783319985657
15 a 20 dias
588
Descrição não disponível.
Introduction.- Part I - Basic Natural Computing Techniques for Unsupervised Learning.- Hard Clustering using Evolutionary Algorithms.- Soft Clustering using Evolutionary Algorithms.- Fuzzy / Rough Set Systems for Unsupervised Learning.- Unsupervised Feature Selection using Evolutionary Algorithms.- Unsupervised Feature Selection using Artificial Neural Networks.- Part II - Advanced Natural Computing Techniques for Unsupervised Learning.- Hybrid Genetic Algorithms for Feature Subset Selection in Model-Based Clustering.- Nature-Inspired Optimization Approaches for Unsupervised Feature Selection.- Co-Evolutionary Approaches for Unsupervised Learning.- Mining Evolving Patterns using Natural Computing Techniques.- Multi-objective Optimization for Unsupervised Learning.- Many-objective Optimization for Unsupervised Learning.- Part III - Applications.- Unsupervised Identification of DNA-binding Proteins using Natural Computing Techniques.- Parallel Solution-based Natural Clustering Techniques on Railway Engineering data.- Natural Computing Techniques for Community Detection on Online Social Networks.- Big Data Challenges and Scalability in Natural Computing for Unsupervised Learning.- Conclusion.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Evolutionary Programming;Differential Evolution;Artificial Immune Systems;Ant Colony Optimization;Self-organizing Systems;Evolutionary Multi-objective Optimization;Runtime Analysis of Natural Computing;DNA Computing;Fuzzy Logic / Rough Set Theory;Artificial Neural Networks;Convolutional Neural Networks;Deep Neural Networks;Ensemble Approaches;Nature-Inspired Clustering;Theoretical Foundation Topics;Big Data Challenges;Engineering Applications;Real-World Application
Introduction.- Part I - Basic Natural Computing Techniques for Unsupervised Learning.- Hard Clustering using Evolutionary Algorithms.- Soft Clustering using Evolutionary Algorithms.- Fuzzy / Rough Set Systems for Unsupervised Learning.- Unsupervised Feature Selection using Evolutionary Algorithms.- Unsupervised Feature Selection using Artificial Neural Networks.- Part II - Advanced Natural Computing Techniques for Unsupervised Learning.- Hybrid Genetic Algorithms for Feature Subset Selection in Model-Based Clustering.- Nature-Inspired Optimization Approaches for Unsupervised Feature Selection.- Co-Evolutionary Approaches for Unsupervised Learning.- Mining Evolving Patterns using Natural Computing Techniques.- Multi-objective Optimization for Unsupervised Learning.- Many-objective Optimization for Unsupervised Learning.- Part III - Applications.- Unsupervised Identification of DNA-binding Proteins using Natural Computing Techniques.- Parallel Solution-based Natural Clustering Techniques on Railway Engineering data.- Natural Computing Techniques for Community Detection on Online Social Networks.- Big Data Challenges and Scalability in Natural Computing for Unsupervised Learning.- Conclusion.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Evolutionary Programming;Differential Evolution;Artificial Immune Systems;Ant Colony Optimization;Self-organizing Systems;Evolutionary Multi-objective Optimization;Runtime Analysis of Natural Computing;DNA Computing;Fuzzy Logic / Rough Set Theory;Artificial Neural Networks;Convolutional Neural Networks;Deep Neural Networks;Ensemble Approaches;Nature-Inspired Clustering;Theoretical Foundation Topics;Big Data Challenges;Engineering Applications;Real-World Application
