Stochastic Gradient Descent and the Prediction of MeSH for PubMed Records.

Stochastic Gradient Descent (SGD) has gained popularity for solving large scale supervised machine learning problems. It provides a rapid method for minimizing a number of loss functions and is applicable to Support Vector Machine (SVM) and Logistic optimizations. However SGD does not provide a convenient stopping criterion. Generally an optimal number of iterations over the data may be determined using held out data. Here we compare stopping predictions based on held out data with simply stopping at a fixed number of iterations and show that the latter works as well as the former for a number of commonly studied text classification problems. In particular fixed stopping works well for MeSH(®) predictions on PubMed(®) records. We also surveyed the published algorithms for SVM learning on large data sets, and chose three for comparison: PROBE, SVMperf, and Liblinear and compared them with SGD with a fixed number of iterations. We find SGD with a fixed number of iterations performs as well as these alternative methods and is much faster to compute. As an application we made SGD-SVM predictions for all MeSH terms and used the Pool Adjacent Violators (PAV) algorithm to convert these predictions to probabilities. Such probabilistic predictions lead to ranked MeSH term predictions superior to previously published results on two test sets.