Optimization of sensing receiver for cognitive radio applications
EURASIP Journal on Advances in Signal Processing - Special issue on dynamic spectrum access for wireless networking
Optimal sensor deployment for value-fusion based detection
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Approximate reduction from AUC maximization to 1-norm soft margin optimization
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Control theoretic sensor deployment approach for data fusion based detection
DCOSS'10 Proceedings of the 6th IEEE international conference on Distributed Computing in Sensor Systems
Exploitation of pairwise class distances for ordinal classification
Neural Computation
| Hi-index | 0.15 |
We consider the problem of learning the ranking function that maximizes a generalization of the Wilcoxon-Mann-Whitney statistic on the training data. Relying on an $\epsilon$-accurate approximation for the error-function, we reduce the computational complexity of each iteration of a conjugate gradient algorithm for learning ranking functions from $\mathcal{O}(m^2)$, to $\mathcal{O}(m)$, where $m$ is the number of training samples. Experiments on public benchmarks for ordinal regression and collaborative filtering indicate that the proposed algorithm is as accurate as the best available methods in terms of ranking accuracy, when the algorithms are trained on the same data. However, since it is several orders of magnitude faster than the current state-of-the-art approaches, it is able to leverage much larger training datasets.