Breast cancer detection using darch

In this section, we will use the darch package, which is used for deep architectures and Restricted Boltzmann Machines (RBM). The darch package is built on the basis of the code from G. E. Hinton and R. R. Salakhutdinov (available under MATLAB code for Deep Belief Nets (DBN)). This package is for generating neural networks with many layers (deep architectures) and training them with the method introduced by the authors. This method includes a pre-training with the contrastive divergence method and fine-tuning with commonly known training algorithms such as backpropagation or conjugate gradients. Additionally, supervised fine-tuning can be enhanced with maxout and dropout, two recently developed techniques used to improve fine-tuning for deep learning.

The basis of the example is classification based on a set of inputs. To do this, we will use the data contained in the dataset named BreastCancer.csv that we just used in Chapter 5, Training and Visualizing...