Feature extraction vs Fine tuning with Restricted Boltmann Machines I am reading a paper which uses a Restricted Boltzmann Machine to extract features from a dataset in an unsupervised way and then use those features to train a classifier (they use SVM but it could be every other). I am a little bit confused about what they call feature extraction and fine-tuning. I don't understand whether there is a difference in the two approaches or if they could be mixed together to reproduce their experiment.
To me, doing feature extraction is training the RBM on a dataset, find relevant hidden features and then use them as input of a classifier, which has no weights (parameters) in common with the RBM. On the other hand, fine tuning is to train the RBM on a dataset and then initialize the weights of a classifier with the same structure (imagine a feedforward NN) with those coming from the trained RBM.
Is there a cross line between those two definition? What I think they do in the paper is feature extraction in the sense I explained above, but they mention also fine-tuning with backpropagation and I would those concepts to be clarified.
 A: Here is a summary of what I get from the article to help clarify your ideas. The authors propose 2 steps : 
1) Learning the relevant features from the data with a Deep Belief Network (DBN) which is a network made of stacked RBMs. This network is trained first unsupervisedly (pretraining to get good initial weights and biases). Then when the authors talk about backpropagation they are fine-tuning their network by turning the DBN into a feedforward NN.
In fact, I think they follow strictly the method they reference : Hinton & Salakhutdinov's Science paper. You may read it, it describes the unsupervised pretraining / supervised fine-tuning procedure, which has since become quite commonly used in machine learning.
2) Learning a mapping from the extracted features to the stock price. They set up another supervised classifier, a SVM, but this could again be a feedforward NN as you mention.
In a few lines of the article a few different concepts are used and mixed (discriminative/generative models, directed/undirected graphical models, unsupervised/supervised learning...) so things can indeed get messy!
