A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Weighted quality estimates in machine learning
2006
Bioinformatics
Motivation: Machine learning methods such as neural networks, support vector machines, and other classification and regression methods rely on iterative optimization of the model quality in the space of the parameters of the method. Model quality measures (accuracies, correlations, etc.) are frequently overly optimistic because the training sets are dominated by particular families and subfamilies. To overcome the bias, the data set is usually reduced by filtering out closely related objects.
doi:10.1093/bioinformatics/btl458
pmid:16935929
fatcat:2rycyl2im5gibf46xobuz3xwvu