Recent developments have demonstrated deep models to be very powerful generative models which are able to extract features automatically and obtain high predictive performance. Typically, a building block of a deep architecture is Restricted Boltzmann Machine (RBM). In this work, we focus on a variant of RBM adopted to the classification setting, which is known as Classification Restricted Boltzmann Machine. We claim that this model should be used as a stand-alone non-linear classifier which could be extremely useful in medical domains. Additionally, we show how to obtain sparse representation in RBM by adding a regularization term to the learning objective which enforces sparse solution. The considered classifier is then applied to five different medical domains.
|Number of pages||7|
|Journal||World Applied Sciences Journal|
|Publication status||Published - 1 Jan 2014|
- Medical domain
- Restricted boltzmann machine