 | 2008 |
| 9 |  | Nicos G. Pavlidis,
Dimitris K. Tasoulis,
Niall M. Adams,
David J. Hand:
Dynamic Multi-Armed Bandit with Covariates.
ECAI 2008: 777-778 |
| 2007 |
| 8 |  | Nicos G. Pavlidis,
E. G. Pavlidis,
Michael G. Epitropakis,
Vassilis P. Plagianakos,
Michael N. Vrahatis:
Computational intelligence algorithms for risk-adjusted trading strategies.
IEEE Congress on Evolutionary Computation 2007: 540-547 |
| 7 |  | Nicos G. Pavlidis,
Michael N. Vrahatis,
P. Mossay:
Existence and computation of short-run equilibria in economic geography.
Applied Mathematics and Computation 184(1): 93-103 (2007) |
| 2006 |
| 6 |  | Dimitris K. Tasoulis,
Panagiota Spyridonos,
Nicos G. Pavlidis,
Vassilis P. Plagianakos,
Panagiota Ravazoula,
George Nikiforidis,
Michael N. Vrahatis:
Cell-nuclear data reduction and prognostic model selection in bladder tumor recurrence.
Artificial Intelligence in Medicine 38(3): 291-303 (2006) |
| 5 |  | Nicos G. Pavlidis,
Dimitris K. Tasoulis,
Vassilis P. Plagianakos,
Michael N. Vrahatis:
Computational Intelligence Methods for Financial Time Series Modeling.
I. J. Bifurcation and Chaos 16(7): 2053-2062 (2006) |
| 4 |  | Vasileios L. Georgiou,
Nicos G. Pavlidis,
Konstantinos E. Parsopoulos,
Philipos D. Alevizos,
Michael N. Vrahatis:
New Self-adaptive Probabilistic Neural Networks in Bioinformatic and Medical Tasks.
International Journal on Artificial Intelligence Tools 15(3): 371-396 (2006) |
| 3 |  | Nicos G. Pavlidis,
Vassilis P. Plagianakos,
Dimitris K. Tasoulis,
Michael N. Vrahatis:
Financial forecasting through unsupervised clustering and neural networks.
Operational Research 6(2): 103-127 (2006) |
| 2005 |
| 2 |  | Nicos G. Pavlidis,
Dimitris K. Tasoulis,
Michael N. Vrahatis:
Time Series Forecasting Methodology for Multiple-Step-Ahead Prediction.
Computational Intelligence 2005: 456-461 |
| 2003 |
| 1 |  | Dimitris K. Tasoulis,
Panagiota Spyridonos,
Nicos G. Pavlidis,
Dionisis Cavouras,
Panagiota Ravazoula,
George Nikiforidis,
Michael N. Vrahatis:
Urinary Bladder Tumor Grade Diagnosis Using On-line Trained Neural Networks.
KES 2003: 199-206 |