Feature selection supervised learning

R. Battiti. Using Mutual Information for Selecting Features in supervised Neural-net Learning. IEEE Transactions on Neural Networks, 5(4) (1994). 537-550. 

Some feature selection methods may be used not only for supervised but also for unsupervised learning however, most methods require the knowledge of the class membership for the training set patterns. 

Battiti, R. (1994) Using mutual information for selecting features in supervised neural net learning. IEEE Transactions Neural Networks, 5, 537-50. 

Both cases can be dealt with both by supervised and unsupervised variants of networks. The architecture and the training of supervised networks for spectra interpretation is similar to that used for calibration. The input vector consists in a set of spectral features yt(Zj) (e.g., intensities at selected wavelengths zi). The output vector contains information on the presence and absence of certain structure elements and groups fixed by learning rules (Fig. 8.24). Various types of ANN models may be used for spectra interpretation, viz mainly such as Adaptive Bidirectional Associative Memory (BAM) and Backpropagation Networks (BPN). The correlation... 

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