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Pattern recognition and machine learning / Christopher M. Bishop.

By: Material type: TextOriginal language: Spanish Series: Information science and statisticsPublisher: Nueva York : Springer, 2009Edition: 1ª ediciónDescription: 738 páginas : figuras ; 24 centímetrosContent type:
  • texto
Media type:
  • sin mediación
Carrier type:
  • volumen
ISBN:
  • 9780387310732
Subject(s): DDC classification:
  • ddc 006.31 B622
Contents:
1. Introducción -- 2. Probabilidad -- 3. Modelos lineales para regresión -- 4. Modelos lineales para clasificación -- 5. Redes neuronales -- 6. Métodos kernel -- 7. Modelos gráficos -- 8. Métodos de aproximación -- 9. Mezclas gaussianas -- 10. Inferencia aproximada -- 11. Muestreo -- 12. Modelos secuenciales -- 13. Modelos de mezcla y aprendizaje no supervisado -- 14. Modelos de variables latentes -- 15. Aprendizaje de máquinas de soporte vectorial -- 16. Reconocimiento de patrones.
Summary: This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful, though not essential, as the book includes a self-contained introduction to basic probability theory.
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Holdings
Cover image Item type Current library Home library Collection Shelving location Call number Materials specified Vol info URL Copy number Status Notes Date due Barcode Item holds Item hold queue priority Course reserves
LIBROS - MATERIAL GENERAL BIBLIOTECA CENTRAL General 006.31 B622 (Browse shelf(Opens below)) Ej.:1 Available (Sin Restricciones) 088972
LIBROS - MATERIAL GENERAL BIBLIOTECA CENTRAL General 006.31 B622 (Browse shelf(Opens below)) Ej.:3 Available (Sin Restricciones) 088973
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Incluye referencias bibliográficas e índice.

1. Introducción -- 2. Probabilidad -- 3. Modelos lineales para regresión -- 4. Modelos lineales para clasificación -- 5. Redes neuronales -- 6. Métodos kernel -- 7. Modelos gráficos -- 8. Métodos de aproximación -- 9. Mezclas gaussianas -- 10. Inferencia aproximada -- 11. Muestreo -- 12. Modelos secuenciales -- 13. Modelos de mezcla y aprendizaje no supervisado -- 14. Modelos de variables latentes -- 15. Aprendizaje de máquinas de soporte vectorial -- 16. Reconocimiento de patrones.

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful, though not essential, as the book includes a self-contained introduction to basic probability theory.

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