Machine learning : an algorithmic perspective

Machine learning an algorithmic perspective

A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning a...

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Detalles Bibliográficos
Otros Autores: Marsland, Stephen, eauthor (eauthor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton, FL : Chapman and Hall/CRC, an imprint of Taylor and Francis 2014.
Edición:Second edition
Colección:Chapman & Hall/CRC machine learning & pattern recognition series.
Materias:
Machine learning.
Algorithms.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629774506719
Tabla de Contenidos:
  • Chapter 1. Introduction
  • Chapter 2. Preliminaries
  • Chapter 3. Neurons, neural networks, and linear discriminants
  • Chapter 4. The multi-layer perceptron
  • Chapter 5. Radial basis functions and splines
  • Chapter 6. Dimensionality reduction
  • Chapter 7. Probabilistic learning
  • Chapter 8. Support vector machines
  • Chapter 9. Optimisation and search
  • Chapter 10. Evolutionary learning
  • Chapter 11. Reinforcement learning
  • Chapter 12. Learning with trees
  • Chapter 13. Decision by committee: ensemble learning
  • Chapter 14. Unsupervised learning
  • Chapter 15. Markov Chain Monte Carlo (MCMC) methods
  • Chapter 16. Graphical models
  • Chapter 17. Symmetric weights and deep belief networks
  • Chapter 18. Gaussian processes
  • Appendix A: .Python.

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