A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Artificial Neural Networks
Learning the Optimum Statistical Model from Data
This chapter provides a general background on machine learning basics to a non‐expert readership with a strong background in computational science. It rephrases the machine learning problem according to an information theory paradigm that highlights the tight entanglement between two perspectives on data science: the statistic versus the optimization ones. The chapter presents the sampling theory, including an introduction on the Fisher approach to statistical learning and an introduction to the principle of maximum entropy for conditional probability laws. It discusses the optimization framework in which major machine learning problems are cast, consisting of minimizing the empirical risk over a class of parametric statistical models. This framework is presented in connection with the Fisher information and the MaxEnt principle. First‐ and second‐order (stochastic) gradient descent optimization methods are presented in detail, with meaningful examples and hands‐on sessions.
Artificial Neural Networks
Learning the Optimum Statistical Model from Data
This chapter provides a general background on machine learning basics to a non‐expert readership with a strong background in computational science. It rephrases the machine learning problem according to an information theory paradigm that highlights the tight entanglement between two perspectives on data science: the statistic versus the optimization ones. The chapter presents the sampling theory, including an introduction on the Fisher approach to statistical learning and an introduction to the principle of maximum entropy for conditional probability laws. It discusses the optimization framework in which major machine learning problems are cast, consisting of minimizing the empirical risk over a class of parametric statistical models. This framework is presented in connection with the Fisher information and the MaxEnt principle. First‐ and second‐order (stochastic) gradient descent optimization methods are presented in detail, with meaningful examples and hands‐on sessions.
Artificial Neural Networks
Learning the Optimum Statistical Model from Data
Stefanou, Ioannis (author) / Darve, Félix (author) / GATTI, Filippo (author)
Machine Learning in Geomechanics 1 ; 145-236
2024-10-25
92 pages
Article/Chapter (Book)
Electronic Resource
English
| Springer Verlag | 2018
Impact Detection Using Artificial Neural Networks
| British Library Online Contents | 2012
Impact Detection Using Artificial Neural Networks
| Tema Archive | 2011
Artificial neural networks for streamflow prediction
| British Library Online Contents | 2002
Site Characterization by Artificial Neural Networks
| British Library Conference Proceedings | 2003