Apply learned boosted decision tree classifier. USAGE hs = adaBoostApply( X, model, [maxDepth], [minWeight], [nThreads] ) INPUTS X - [NxF] N length F feature vectors model - learned boosted tree classifier maxDepth - [] maximum depth of tree minWeight - [] minimum sample weigth to allow split nThreads - [16] max number of computational threads to use OUTPUTS hs - [Nx1] predicted output log ratios EXAMPLE See also adaBoostTrain Piotr's Computer Vision Matlab Toolbox Version 3.40 Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] Licensed under the Simplified BSD License [see external/bsd.txt]