Aritz Pérez
Zitiert von
Zitiert von
Sensitivity analysis of k-fold cross validation in prediction error estimation
JD Rodriguez, A Perez, JA Lozano
IEEE transactions on pattern analysis and machine intelligence 32 (3), 569-575, 2009
Machine learning in bioinformatics
P Larranaga, B Calvo, R Santana, C Bielza, J Galdiano, I Inza, JA Lozano, ...
Briefings in bioinformatics 7 (1), 86-112, 2006
An efficient approximation to the K-means clustering for massive data
M Capó, A Pérez, JA Lozano
Knowledge-Based Systems 117, 56-69, 2017
Bayesian classifiers based on kernel density estimation: Flexible classifiers
A Pérez, P Larrañaga, I Inza
International Journal of Approximate Reasoning 50 (2), 341-362, 2009
Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes
A Perez, P Larranaga, I Inza
International Journal of Approximate Reasoning 43 (1), 1-25, 2006
Fish recruitment prediction, using robust supervised classification methods
JA Fernandes, X Irigoien, N Goikoetxea, JA Lozano, I Inza, A Pérez, ...
Ecological Modelling 221 (2), 338-352, 2010
An efficient K-means clustering algorithm for tall data
M Capó, A Pérez, JA Lozano
Data mining and knowledge discovery 34, 776-811, 2020
Using multidimensional bayesian network classifiers to assist the treatment of multiple sclerosis
JD Rodriguez, A Perez, D Arteta, D Tejedor, JA Lozano
IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and …, 2012
Supervised pre-processing approaches in multiple class variables classification for fish recruitment forecasting
JA Fernandes, JA Lozano, I Inza, X Irigoien, A Pérez, JD Rodríguez
Environmental modelling & software 40, 245-254, 2013
A general framework for the statistical analysis of the sources of variance for classification error estimators
JD Rodríguez, A Pérez, JA Lozano
Pattern recognition 46 (3), 855-864, 2013
On-line dynamic time warping for streaming time series
I Oregi, A Pérez, J Del Ser, JA Lozano
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2017
A Cheap Feature Selection Approach for the K-Means Algorithm
M Capó, A Pérez, JA Lozano
IEEE transactions on neural networks and learning systems 32 (5), 2195-2208, 2020
An efficient Split-Merge re-start for the K-means algorithm
M Capo, A Perez, JAA Lozano
IEEE Transactions on Knowledge and Data Engineering, 2020
An efficient K-means clustering algorithm for massive data
M Capó, A Pérez, JA Lozano
arXiv preprint arXiv:1801.02949, 2018
How trustworthy is Crafty’s analysis of world chess champions?
M Guid, A Pérez, I Bratko
ICGA journal 31 (3), 131-144, 2008
Adversarial sample crafting for time series classification with elastic similarity measures
I Oregi, J Del Ser, A Perez, JA Lozano
Intelligent Distributed Computing XII, 26-39, 2018
Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species
JA Fernandes, X Irigoien, JA Lozano, I Inza, N Goikoetxea, A Pérez
Ecological Informatics 25, 35-42, 2015
On-line elastic similarity measures for time series
I Oregi, A Pérez, J Del Ser, JA Lozano
Pattern Recognition 88, 506-517, 2019
The potential use of a Gadget model to predict stock responses to climate change in combination with Bayesian networks: the case of Bay of Biscay anchovy
E Andonegi, JA Fernandes, I Quincoces, X Irigoien, A Uriarte, A Pérez, ...
ICES Journal of Marine Science 68 (6), 1257-1269, 2011
Minimax Classification with 0-1 Loss and Performance Guarantees
S Mazuelas, A Zanoni, A Pérez
Neural Information Processing Systems, 2020
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