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...
Guardado en:
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| Otros Autores: | |
| Formato: | Libro electrónico |
| Lenguaje: | Inglés |
| Publicado: |
London :
Springer London : Imprint: Springer,
2014.
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| Materias: | |
| Acceso en línea: | http://dx.doi.org/10.1007/978-1-4471-5571-3 |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
Tabla de Contenidos:
- 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.