Local lazy regression

The development of new data analysis methods is also an important area of QSAR research. Several methods have been developed in recent years, and these include kernel partial least squares (K-PLS) [92], robust continuum regression [93], local lazy regression [94], fuzzy interval number -nearest neighbor (FINkNN) [95], and fast projection plane classifier (FPPC) [96], These methods have been shown to be useful for the prediction of a wide variety of target properties, which include moisture, oil, protein and starch... 

Guha R, Dutta D, Jurs PC, Chen T. Local lazy regression Making use of the neighborhood to improve QSAR predictions. J Chem Inf Model 2006 46 1836-47. 

Guha, R., Dutta, D., Jurs, R C., Chen, T. Local lazy regression making use of the neighbourhood to improve QSAR predictions. J. Chem. Inf Model. 2006, 46, 1836-1847. 

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