GH learning algorithm.
GH - Gaussian Herding (with variants full, exact, drop and project)
Reference: Crammer, K., Lee, D.: Learning via Gaussian Herding. Advances in Neural Information Processing Systems 23, 414-422 (2010), http://webee.technion.ac.il/people/koby/publications/gaussian_mob_nips10.pdf
Documentation
algorithms/icl_learn_GH
ILS = icl_learn_GH(ILS, x, y, yp, mode)
Inputs:
| ILS | structure describing the learning system |
| x | double input vector in [-10,10] |
| y | true label, i.e. target value of the example, in [-1, 1] for regression or [-1; 1] for classification |
| yp | predicted label, i.e. current output of the approximation, in [-1, 1] for regression or [-1; 1] for classification |
| mode | operating mode (regression=1, classification=2) |
Outputs:
| ILS | updated matlab structure describing the learning system |
Calls:
Called by:
- none
Authors:
- Jan Hendrik Schoenke (jschoenk(at)uos.de)
- Andreas Buschermöhle (andbusch(at)uos.de)
Last change: 2013-07-13 - Version 1.5