Neural Networks and Statistical Learning
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...
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
London
Springer London
2014, 2014
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| Edition: | 1st ed. 2014 |
| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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