Neural Networks and Statistical Learning

• Κύριοι συγγραφείς: Du, Ke-Lin (Συγγραφέας), Swamy, M. N. S. (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: London : Springer London : Imprint: Springer, 2014.
• Θέματα: Engineering.; Data mining.; Pattern recognition.; Neural networks (Computer science).; Computational intelligence.; Computational Intelligence.; Mathematical Models of Cognitive Processes and Neural Networks.; Data Mining and Knowledge Discovery.; Pattern Recognition.

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples...

Πίνακας περιεχομένων:

• Introduction
• Fundamentals of Machine Learning
• Perceptrons
• Multilayer perceptrons: architecture and error backpropagation
• Multilayer perceptrons: other learing techniques
• Hopfield networks, simulated annealing and chaotic neural networks
• Associative memory networks
• Clustering I: Basic clustering models and algorithms
• Clustering II: topics in clustering
• Radial basis function networks
• Recurrent neural networks
• Principal component analysis
• Nonnegative matrix factorization and compressed sensing
• Independent component analysis
• Discriminant analysis
• Support vector machines
• Other kernel methods
• Reinforcement learning
• Probabilistic and Bayesian networks
• Combining multiple learners: data fusion and emsemble learning
• Introduction of fuzzy sets and logic
• Neurofuzzy systems
• Neural circuits
• Pattern recognition for biometrics and bioinformatics
• Data mining.