 | 2009 |
| 11 |  | Sergio Muñoz-Romero,
Jerónimo Arenas-García,
Vanessa Gómez-Verdejo:
Real Adaboost Ensembles with Emphasized Subsampling.
IWANN (1) 2009: 440-447 |
| 10 |  | Vanessa Gómez-Verdejo,
Michel Verleysen,
Jérôme Fleury:
Information-theoretic feature selection for functional data classification.
Neurocomputing 72(16-18): 3580-3589 (2009) |
| 2008 |
| 9 |  | Iván González-Díaz,
Dario García-García,
Rubén Solera-Ureña,
Jaisiel Madrid-Sánchez,
Vanessa Gómez-Verdejo,
Manel Martínez-Ramón,
Fernando Díaz-de-María,
Jerónimo Arenas-García:
UC3M High Level Feature Extraction at TRECVID 2008.
TRECVID 2008 |
| 8 |  | Vanessa Gómez-Verdejo,
Jerónimo Arenas-García,
Aníbal R. Figueiras-Vidal:
A Dynamically Adjusted Mixed Emphasis Method for Building Boosting Ensembles.
IEEE Transactions on Neural Networks 19(1): 3-17 (2008) |
| 2007 |
| 7 |  | Vanessa Gómez-Verdejo,
Michel Verleysen,
Jérôme Fleury:
Information-Theoretic Feature Selection for the Classification of Hysteresis Curves.
IWANN 2007: 522-529 |
| 6 |  | Jerónimo Arenas-García,
Vanessa Gómez-Verdejo,
Aníbal R. Figueiras-Vidal:
Fast evaluation of neural networks via confidence rating.
Neurocomputing 70(16-18): 2775-2782 (2007) |
| 2006 |
| 5 |  | Vanessa Gómez-Verdejo,
Aníbal R. Figueiras-Vidal:
Designing neural network committees by combining boosting ensembles.
ESANN 2006: 419-424 |
| 4 |  | Vanessa Gómez-Verdejo,
Manuel Ortega-Moral,
Jerónimo Arenas-García,
Aníbal R. Figueiras-Vidal:
Boosting by weighting critical and erroneous samples.
Neurocomputing 69(7-9): 679-685 (2006) |
| 2005 |
| 3 |  | Manuel Ortega-Moral,
Vanessa Gómez-Verdejo,
Jerónimo Arenas-García,
Aníbal R. Figueiras-Vidal:
An On-line Fisher Discriminant.
ESANN 2005: 175-180 |
| 2 |  | Vanessa Gómez-Verdejo,
Manuel Ortega-Moral,
Jerónimo Arenas-García,
Aníbal R. Figueiras-Vidal:
Boosting by weighting boundary and erroneous samples.
ESANN 2005: 85-90 |
| 1 |  | Jerónimo Arenas-García,
Vanessa Gómez-Verdejo,
Sergio Muñoz-Romero,
Manuel Ortega-Moral,
Aníbal R. Figueiras-Vidal:
Fast Classification with Neural Networks via Confidence Rating.
IWANN 2005: 622-629 |