AROW learning algorithm.
AROW - Adaptive Regularization of Weight Vectors
Reference: Crammer, K., Kulesza, A., Dredze, M., et al.: Adaptive regularization of weight vectors. Advances in Neural Information Processing Systems 22, 414-422 (2009), http://www.cis.upenn.edu/~kulesza/pubs/arow_nips09.pdf
Documentation
algorithms/icl_learn_AROW
ILS = icl_learn_AROW(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:
- Sebastian Pütz (spuetz(at)uos.de)
- Andreas Buschermöhle (andbusch(at)uos.de)
Last change: 2013-07-13 - Version 1.5