Pattern-recognition importance sampling minimization

Pattern-Recognition Importance Sampling Minimization (PRISM)... 

M. H. Lambert and H. A. Scheraga, J. Comput. Chem., 10,817 (1989). Pattern Recognition in the Prediction of Protein Structure. III. An Importance-Sampling Minimization Procedure. 

The choice of the training set is important in any pattern-recognition study. Each class must be well represented in the training set. Experimental variables must be controlled or otherwise accounted for by the selection of suitable samples that take into account all sources of variability in the data, for example, lot-to-lot variability. Experimental artifacts such as instrumental drift or sloping baseline must be minimized. Features containing information about differences in the source profile of each class must be present in the data. Otherwise, the classifier is likely to discover rules that do not work well on test samples, i.e., samples that are not part of the original data. 

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See also in sourсe #XX -- [ ]

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