6 citations · 6 across the 2 of their papers we have counts for
3 papers
A generalized linear joint trained framework for semi-supervised learning of sparse features
Juan C. Laria, Line H. Clemmensen, Bjarne K. Ersbøll
The elastic-net is among the most widely used types of regularization algorithms, commonly associated with the problem of supervised generalized linear model estimation via penaliz…
Deep learning for Chemometric and non-translational data
Jacob Søgaard Larsen, Line Clemmensen
We propose a novel method to train deep convolutional neural networks which learn from multiple data sets of varying input sizes through weight sharing. This is an advantage in che…
Forest Floor Visualizations of Random Forests
Soeren H. Welling, Hanne H. F. Refsgaard, Per B. Brockhoff +1
We propose a novel methodology, forest floor, to visualize and interpret random forest (RF) models. RF is a popular and useful tool for non-linear multi-variate classification and…