Notice that there is a large performance improvement after the first step (which alone is a linear solver), but overfitting occurs after step 11.

This might be a result of overfitting the model to a single response variable which usually has a smooth behaviour.

On the contrary, the multitask learning property of BGL reduces this type of overfitting providing more statistical evidence for the terms and users and thus, yielding not only a better inference performance, but also a more accurate model.

Although flexible, this approach would be doomed to failure due to the sheer size of the resulting feature set, and the propensity to overfit all but the largest of training sets.

The El-norm regularisation has found many applications in several scientific fields as it encourages sparse solutions which reduce the possibility of overfitting and enhance the interpretability of the inferred model (Hastie et al., 2009).

PLSA solves the polysemy problem; however it is not considered a fully generative model of documents and it is known to be overfitting (Blei et al., 2003).

LDA performs better than PLSA for small datasets since it avoids overfitting and it supports polysemy (Blei et al., 2003).

LDA was chosen to generate the topic models of clinical reports due to its being a generative probabilistic system for documents and its robustness to overfitting .

SVM was chosen as the classification algorithm as it was shown that it performs well in text classification tasks (J oachims, 1998; Yang and Liu, 1999) and it is robust to overfitting (Sebastiani, 2002).