A. Abraham, E. Corchado, and J. M. Corchado, Hybrid learning machines, Neurocomputing, vol.72, issue.13-15, pp.2729-2730, 2009.
DOI : 10.1016/j.neucom.2009.02.017

Á. Herrero, E. Corchado, M. A. Pellicer, and A. Abraham, MOVIH-IDS: A mobile-visualization hybrid intrusion detection system, Neurocomputing, vol.72, issue.13-15, pp.2775-2784, 2009.
DOI : 10.1016/j.neucom.2008.12.033

URL : http://gicap.ubu.es/publications/2009/PDF/2009_c01_MOVIH-IDS.pdf

I. Watson, Case-based reasoning is a methodology not a technology. Knowledge- Based Systems, pp.303-308, 1999.
DOI : 10.1016/s0950-7051(99)00020-9

URL : http://www.cs.auckland.ac.nz/~ian/papers/cbr/methodology.pdf

T. Kohonen, The self-organizing map, Neurocomputing, vol.21, issue.1-3, pp.1-6, 1998.
DOI : 10.1016/S0925-2312(98)00030-7

B. Baruque and E. Corchado, A weighted voting summarization of SOM ensembles, Data Mining and Knowledge Discovery, vol.14, issue.1, pp.1-29
DOI : 10.1007/978-3-642-97610-0

A. Aamodt, A Knowledge-Intensive, Integrated Approach to Problem Solving and Sustained Learning. Knowledge Engineering and Image Processing Group, 1991.

A. Aamodt and E. Plaza, Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications, pp.39-59, 1994.

B. Baruque, E. Corchado, A. Mata, and J. M. Corchado, A forecasting solution to the oil spill problem based on a hybrid intelligent system, Information Sciences, vol.180, issue.10, pp.2029-2043, 2010.
DOI : 10.1016/j.ins.2009.12.032

A. Mata and J. M. Corchado, Forecasting the probability of finding oil slicks using a CBR system, Expert Systems with Applications, vol.36, issue.4, pp.8239-8246, 2009.
DOI : 10.1016/j.eswa.2008.10.003

B. S. Yang, T. Han, and Y. S. Kim, Integration of ART-Kohonen neural network and case-based reasoning for intelligent fault diagnosis, Expert Systems with Applications, vol.26, issue.3, pp.387-395, 2004.
DOI : 10.1016/j.eswa.2003.09.009

F. Diaz, F. Fdez-riverola, and J. M. Corchado, gene-CBR: A CASE-BASED REASONIG TOOL FOR CANCER DIAGNOSIS USING MICROARRAY DATA SETS, Computational Intelligence, vol.5, issue.2, pp.254-268, 2006.
DOI : 10.1126/science.270.5235.467

C. Liu, L. Chen, and C. Hsu, An association-based case reduction technique for case-based reasoning, Information Sciences, vol.178, issue.17, pp.3347-3355, 2008.
DOI : 10.1016/j.ins.2008.05.006

G. Pölzlbauer, Survey and Comparison of Quality Measures for Self-Organizing Maps, 2004.

J. M. Corchado and F. Fdez-riverola, FSfRT: Forecasting System for Red Tides, Applied Intelligence, vol.21, pp.251-264, 2004.

N. B. Karayiannis and G. W. Mi, Growing radial basis neural networks, Proceedings of International Conference on Neural Networks (ICNN'97), pp.1492-1506, 1997.
DOI : 10.1109/ICNN.1997.614000

F. Sørmo, J. Cassens, and A. Aamodt, Explanation in Case-Based Reasoning???Perspectives and Goals, Artificial Intelligence Review, vol.54, issue.1???2, pp.109-143, 2005.
DOI : 10.1007/978-3-642-77927-5_24

D. G. Long, Mapping fire regimes across time and space: Understanding coarse and fine-scale fire patterns, International Journal of Wildland Fire, vol.10, pp.329-342, 2001.

G. Mazzeo, F. Marchese, C. Filizzola, N. Pergola, and V. Tramutoli, A Multitemporal Robust Satellite Technique (RST) for Forest Fire Detection. Analysis of Multitemporal Remote Sensing Images, 2007.
DOI : 10.1109/multitemp.2007.4293060

B. C. Arrue, A. Ollero, and J. R. Matinez-de-dios, An intelligent system for false alarm reduction in infrared forest-fire detection. Intelligent Systems and Their Applications, IEEE, vol.15, pp.64-73, 2000.

C. Muñoz, P. Acevedo, S. Salvo, G. Fagalde, and F. Vargas, Forest fire detection using NOAA/16-LAC satellite images in the Araucanía Region, Chile. Bosque, vol.28, pp.119-128, 2007.

R. Rodríguez, A. Cortés, T. Margalef, and E. Luque, An Adaptive System for Forest Fire Behavior Prediction, 2008 11th IEEE International Conference on Computational Science and Engineering, 2008.
DOI : 10.1109/CSE.2008.15

L. S. Iliadis, A decision support system applying an integrated fuzzy model for longterm forest fire risk estimation. Environmental Modelling and Software, pp.613-621, 2005.
DOI : 10.1016/j.envsoft.2004.03.006