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Online Multi-class Learning Description: This code computes multiple online random ferns classifiers with small human supervision for a 2D multi-class problem. The proposed algorithm uses active learning in combination with an adaptive uncertainty threshold in order to reduce the degree of human assistance as the classifier confidence gets larger. To keep efficiency, all classifiers are computed with the same ferns features. For more detail refer to reference [1]. Comments: The parameters of the classifiers and the 2D feature scenario can be found in the fun_parameters.m function. In this code, the training and test samples are generated at random. The positive samples correspond to K different clusters whereas the negative samples are spread over the entire 2D feature space. If you make use of this code for research articles, we kindly encourage to cite the reference [1], listed below. This code is only for research and educational purposes. Steps: Steps to exucute the program: 1. Run the prg_setup.m file to configure the program paths. 2. Run the prg_online_multiclass_learning.m file to compute the classifiers and to perfom classification on the 2D problem scenario. References: [1] Modeling Robot's World with Minimal Effort M. Villamizar, A. Garrell, A. Sanfeliu and F. Moreno-Noguer International Conference on Robotics and Automation (ICRA) Seattle, USA, July 2015 Contact: Michael Villamizar mvillami-at-iri.upc.edu Institut de Robòtica i Informática Industrial CSIC-UPC Barcelona - Spain 2016