Javier Bejar
Cited by
Cited by
Clustering algorithm for determining community structure in large networks
JM Pujol, J Béjar, J Delgado
Physical Review E 74 (1), 016107, 2006
Generality-based conceptual clustering with probabilistic concepts
L Talavera, J Béjar
IEEE Transactions on pattern analysis and machine intelligence 23 (2), 196-206, 2001
Concept formation in WWTP by means of classification techniques: a compared study
M Sànchez, U Cortés, J Béjar, JD Grácia, J Lafuente, M Poch
Applied Intelligence 7 (2), 147-165, 1997
Aprendizaje automático
A Moreno, E Armengol, J Béjar Alonso, LA Belanche Muñoz, ...
Edicions UPC, 1994
K-means vs Mini Batch K-means: a comparison
J Béjar Alonso
Integrating declarative knowledge in hierarchical clustering tasks
L Talavera, J Béjar
Advances in Intelligent Data Analysis, 211-222, 1999
DAI-DEPUR: an integrated and distributed architecture for wastewater treatment plants supervision
M Sànchez, U Cortés, J Lafuente, IR Roda, M Poch
Artificial Intelligence in Engineering 10 (3), 275-285, 1996
Wind energy forecasting with neural networks: A literature review
J Manero, J Béjar, U Cortés
Computación y Sistemas 22 (4), 1085-1098, 2018
Discovery of Spatio-Temporal Patterns from Location Based Social Networks.
J Bejar, S Álvarez-Napagao, D Garcia-Gasulla, I Gómez-Sebastià, L Oliva, ...
CCIA, 126-135, 2014
Nearest-neighbours for time series
JMG Illa, JB Alonso, MS Marré
Applied Intelligence 20 (1), 21-35, 2004
LINNEO+: Herramienta para la adquisición de conocimiento y generación de reglas de clasificación en dominios poco estructurados
J Béjar, U Cortés
Actas del III Congreso Iberoamericano de Inteligencia Artificial (IBERAMIA92), 1992
“Dust in the wind...”, deep learning application to wind energy time series forecasting
J Manero, J Béjar, U Cortés
Energies 12 (12), 2385, 2019
A distributed control system based on agent architecture for wastewater treatment
J Baeza, D Gabriel, J Béjar, J Lafuente
Computer‐Aided Civil and Infrastructure Engineering 17 (2), 93-103, 2002
Supraspinal modulation of neuronal synchronization by nociceptive stimulation induces an enduring reorganization of dorsal horn neuronal connectivity
E Contreras‐Hernández, D Chávez, E Hernández, E Velázquez, P Reyes, ...
The Journal of Physiology 596 (9), 1747-1776, 2018
A machine learning methodology for the selection and classification of spontaneous spinal cord dorsum potentials allows disclosure of structured (non-random) changes in …
M Martin, E Contreras-Hernández, J Béjar, G Esposito, D Chávez, ...
Frontiers in neuroinformatics 9, 21, 2015
Social network data analysis for event detection
D Garcia-Gasulla, S Alvarez-Napagao, A Tejeda-Gómez, L Oliva-Felipe, ...
ECAI 2014, 1009-1010, 2014
LINNEO+: A classification methodology for ill-structured domains
JB Alonso, M Poch, U Cortés
Universitat Politècnica de Catalunya, 1993
A visual embedding for the unsupervised extraction of abstract semantics
D Garcia-Gasulla, E Ayguadé, J Labarta, J Béjar, U Cortés, T Suzumura, ...
Cognitive Systems Research 42, 73-81, 2017
Agent strategies on dpb auction tournaments
J Béjar, U Cortes
Agent-Mediated Electronic Commerce III, 155-172, 2001
Markovian analysis of the sequential behavior of the spontaneous spinal cord dorsum potentials induced by acute nociceptive stimulation in the anesthetized cat
M Martin, J Béjar, G Esposito, D Chávez, E Contreras-Hernández, ...
Frontiers in computational neuroscience 11, 32, 2017
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