• No se han encontrado resultados

Statistical postprocessing of different variables for airports in Spain using machine learning

N/A
N/A
Protected

Academic year: 2020

Share "Statistical postprocessing of different variables for airports in Spain using machine learning"

Copied!
15
0
0

Texto completo

Loading

Referencias

Documento similar

In all cases, measures of thickness (average thickness in the case of random forest and log 10 of maximum thickness in the case of ANN and multiple linear regression) are

The recent exponential growing of machine learning techniques (data-mining, deep-learning, manifold learning, linear and nonlinear regression techniques ... to cite but a few) makes

Figure 4.6: Bar plot comparing the MAE of the five regression models used in this work for the DS2 Dataset when considering different subsets of sensors.The x-axis represents the

For pruning the dataset, we have removed in each step the 10% 1 of the initial number of features and compute the time needed (used to train and predict three times, the errors) and

Third, a new discriminative technique is proposed for incrementally learning the scaling factors λ and the feature functions h, which relies on the concept of Ridge regression,

SVMs applied to a regression problem perform a linear regression in the feature space using the -insensitive loss as cost function and, at the same time, regularizing the weights

Those variables whose categories showed statistical significance to be included in the second regression were: university, having a partner, attended antenatal classes,

(2021), en la investigación, Machine learning regression model for material synthesis prices prediction in agriculture, en India, plantea el problema ¿cómo predecir el