Statistical analysis techniques in particle physics : fits, density estimation and supervised learning / Ilya Narsky, Frank C. Porter
Tipo de material: TextoIdioma: Inglés Fecha de copyright: Weinheim : Wiley-VCH, 2014Edición: First editionDescripción: 441 pages : illustrations, figures ; 24 cmISBN:- 9783527410866
- 23 539.72015195
Tipo de ítem | Biblioteca actual | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras | Reserva de ítems | |
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Colección general | Biblioteca Yachay Tech | 539.72015195 N235s 2014 (Navegar estantería(Abre debajo)) | Ej. 1 | Disponible | 005803 |
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539.72 P448i 2000 Introduction to high energy physics / | 539.72 S1589i 1964 Invariance principles and elementary particles / | 539.72 S7853 2016 The standard theory of particle physics : essays to celebrate CERN's 60th anniversary / | 539.72015195 N235s 2014 Statistical analysis techniques in particle physics : fits, density estimation and supervised learning / | 539.72078 L576t 1994 Techniques for nuclear and particle physics experiments : | 539.721 C51891g 1984 Gauge theory of elementary particle physics / | 539.721 C6927a 1995 Aspects of symmetry : |
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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