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Statistical analysis techniques in particle physics : fits, density estimation and supervised learning / Ilya Narsky, Frank C. Porter

Por: Colaborador(es): Tipo de material: TextoTextoIdioma: Inglés Fecha de copyright: Weinheim : Wiley-VCH, 2014Edición: First editionDescripción: 441 pages : illustrations, figures ; 24 cmISBN:
  • 9783527410866
Tema(s): Clasificación CDD:
  • 23 539.72015195
Contenidos parciales:
Why We Wrote This Book and How You Should Read It -- Parametric Likelihood Fits -- Goodness of Fit -- Resampling Techniques -- Density Estimation -- Basic Concepts and Definitions of Machine Learning -- Data Preprocessing -- Linear Transformations and Dimensionality Reduction -- Introduction to Classification -- Assessing Classifier Performance -- Linear and Quadratic Discriminant Analysis, Logistic Regression, and Partial Least Squares Regression -- Neural Networks -- Local Learning and Kernel Expansion -- Decision Trees -- Ensemble Learning -- Reducing Multiclass to Binary -- How to Choose the Right Classifier for Your Analysis and Apply It Correctly -- Methods for Variable Ranking and Selection -- Bump Hunting in Multivariate Data -- Software Packages for Machine Learning.
Resumen: Modern analysis of HEP data needs advanced statistical tools to separate signal from background. This is the first book which focuses on machine learning techniques. It will be of interest to almost every high energy physicist, and, due to its coverage, suitable for students.
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Tipo de ítem Biblioteca actual Signatura Copia número Estado Fecha de vencimiento Código de barras Reserva de ítems
Colección general Colección general Biblioteca Yachay Tech 539.72015195 N235s 2014 (Navegar estantería(Abre debajo)) Ej. 1 Disponible 005803
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Why We Wrote This Book and How You Should Read It -- Parametric Likelihood Fits -- Goodness of Fit -- Resampling Techniques -- Density Estimation -- Basic Concepts and Definitions of Machine Learning -- Data Preprocessing -- Linear Transformations and Dimensionality Reduction -- Introduction to Classification -- Assessing Classifier Performance -- Linear and Quadratic Discriminant Analysis, Logistic Regression, and Partial Least Squares Regression -- Neural Networks -- Local Learning and Kernel Expansion -- Decision Trees -- Ensemble Learning -- Reducing Multiclass to Binary -- How to Choose the Right Classifier for Your Analysis and Apply It Correctly -- Methods for Variable Ranking and Selection -- Bump Hunting in Multivariate Data -- Software Packages for Machine Learning.

Modern analysis of HEP data needs advanced statistical tools to separate signal from background. This is the first book which focuses on machine learning techniques. It will be of interest to almost every high energy physicist, and, due to its coverage, suitable for students.

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