Regularization in Deep Learning is very important to overcome overfitting. When your training accuracy is very high, but test ...

Many regression analyses involve explanatory variables that are measured with error, and failing to account for this error is well known to lead to biased point and ...

We consider estimation and variable selection in high-dimensional Cox regression when a prior knowledge of the relationships among the covariates, described by a network or graph, is available. A ...

Our data science expert continues his exploration of neural network programming, explaining how regularization addresses the problem of model overfitting, caused by network overtraining. Neural ...

A research team shows that phenomic prediction, which integrates full multispectral and thermal information rather than ...