Deep Belief Networks and Biomedical Text Categorisation
Antonio Jimeno Yepes, Andrew MacKinlay, Justin Bedő, Rahil Garvani, Qiang Chen · 2014
We evaluate the use of Deep Belief Networks as classifiers in a text categorisation task (assigning category labels to documents) in the biomedical domain. Our preliminary results indicate that compared to Support Vector Machines, Deep Belief Networks are superior when a large set of training examples is available, showing an F-score increase of up to 5%. In addition, the training times for DBNs can be prohibitive. DBNs show promise for certain types of biomedical text categorisation.