Plus L take away R (+L-R) google
The “Plus L take away R” (+L -R) is basically a combination of SFS and SBS. It append features to the feature subset L-times, and afterwards it removes features R-times until we reach our desired size for the feature subset.
Variant 1: L > R
If L > R, the algorithm starts with an empty feature subset and adds L features to it from the feature space. Then it goes over to the next step 2, where it removes R features from the feature subset, after which it goes back to step 1 to add L features again. Those steps are repeated until the feature subset reaches the desired size k.
Variant 2: R > L
Else, if R > L, the algorithms starts with the whole feature space* as feature subset. It remove sR features from it before it adds back L features from those features that were just removed.
Those steps are repeated until the feature subset reaches the desired size k*. …


Wake-Sleep Algorithm google
The wake-sleep algorithm is an unsupervised learning algorithm for a multilayer neural network (e.g. sigmoid belief net). Training is divided into two phases, ‘wake’ and ‘sleep’. In the ‘wake’ phase, neurons are driven by recognition connections (connections from what would normally be considered an input to what is normally considered an output), while generative connections (those from outputs to inputs) are modified to increase the probability that they would reconstruct the correct activity in the layer below (closer to the sensory input). In the ‘sleep’ phase the process is reversed: neurons are driven by generative connections, while recognition connections are modified to increase the probability that they would produce the correct activity in the layer above (further from sensory input).
GitXiv


Silhouette google
Silhouette refers to a method of interpretation and validation of clusters of data. The technique provides a succinct graphical representation of how well each object lies within its cluster. It was first described by Peter J. Rousseeuw in 1986. …

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