Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to
Pedestrians detection using Hough forests This a linux port of the original code provided by Olga Barinova from the Vision Group at Moscow State University, 2010.
Topics Covered The topics covered in this book are An overview of decision trees and random forests A manual example of how a human would classify a dataset, compared to how a decision tree would work How a decision tree works, and why it is prone t
