knntlOptimisation(primary, auxiliary, fcol = "markers", k, times = 50, test.size = 0.2, xval = 5, by = 0.5, length.out, th, xfolds, BPPARAM = BiocParallel::bpparam(), method = "Breckels", seed)
"MSnSet"
."MSnSet"
.markers
.k
parameters to use for the primary (k[1]
) and auxiliary
(k[2]
) datasets. See knnOptimisation
for generating
best k
.c(1, 0.5,
0.25, 0.2, 0.15, 0.1, 0.05)
by
parameter. Specifies the desired length of the sequence of theta
to test.thetas
, the number of
columns should be equal to the number of classes contained in
fcol
. Note: columns will be ordered according to
getMarkerClasses(primary, fcol)
. This argument is only
valid if the default method 'Breckels' is used.BiocParallelParam
, from global options
or, if that fails, the most recently registered() back-end.knntlOptimisation
implements a variation of Wu and
Dietterich's transfer learning schema: P. Wu and
T. G. Dietterich. Improving SVM accuracy by training on auxiliary
data sources. In Proceedings of the Twenty-First International
Conference on Machine Learning, pages 871 - 878. Morgan Kaufmann,
2004. A grid search for the best theta is performed.
Wu P, Dietterich TG. Improving SVM Accuracy by Training on Auxiliary Data Sources. Proceedings of the 21st International Conference on Machine Learning (ICML); 2004.
knntlClassification
and example therein.