trainIterOcc: Train a iterative one-class classifier.

In benmack/iterOneClass: Iterative one-class classification for efficiently exploit unlabeled data

Description

...

Usage

trainIterOcc(P, U, nUnTrain = 100, M = NULL, seed = NULL,
  resamplingMethod = "createFoldsPu", k = 10, indepUn = 0.5,
  lastModel = NULL, lastThreshold = NULL, ...)

Arguments

P	a data frame with the positive training samples.
U	a raster of rasterTiled object with the full image data to be classified.
M	a mask for the unlabeled samples in U. only required of U is araster object.
seed	a seed point
resamplingMethod	a character, currently only createFoldsPu supported
indepUn	only used if resamplingMethod is createFoldsPu. The fraction of the unlabeled training data to be used for validation (0 < indepUn < 1).
lastModel	...
lastThreshold	...

benmack/iterOneClass documentation built on May 12, 2019, 12:57 p.m.