Training unsupervised

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... 

Two generally different scenarios can be found for applications of machine learning technology so-called supervised and unsupervised learning. The difference is the presence or absence of observation of the desired output on a training data set. 

Unsupervised multivariate statistical methods [CA, principal components analysis, Kohonen s self-organizing maps (SOMs), nonlinear mapping, etc.], which perform spontaneous data analysis without the need for special training (learning), levels of knowledge, or preliminary conditions. 

K-nearest neighbors (KNN) is the name of a classification method that is unsupervised in the sense that class membership is not used to train the technique, but that makes predictions of class membership. The principle behind KNN is an appealingly common sense one objects that are close together in the descriptor space are expected to show similar behavior in their response. Figure 7.5 shows a two-dimensional representation of the way that KNN operates. 

It can thus be seen that training radial basis function networks uses both supervised and unsupervised learning determining basis function parameters is unsupervised and solution of the linear equations is supervised. 

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