SOM toolbox for Matlab 5 Why? The ”SOM Toolbox” project was initialized back in 1997 because there was no such thing as a proper SOM-library for MATLAB. MATLAB is a magnificient computing environment, but the tools in its neural networks toolbox for SOM were not really up to the state-of-the-art. On the other hand, the freeware SOM program package SOM_PAK is all well and good, but it’s not nearly as flexible as the MATLAB environment. So the plan was to offer a simple, well documented MATLAB function package which is easy to use and modify in the diverse needs that different people unavoidably have. Who? Numerous people have participated in the making of SOM Toolbox. In addition to the people listed in the copyright notice, Kimmo Kiviluoto, Jukka Parviainen, Mika Pollari have participated in it, not to mention those who have contributed code, given bug reports or other feedback. The common factor for us is to have been employed in the Laboratory of Information and Computer Science in the Helsinki University of Technology.

References in zbMATH (referenced in 46 articles )

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  1. Izquierdo, Joaquín; Campbell, Enrique; Montalvo, Idel; Pérez-García, Rafael: Injecting problem-dependent knowledge to improve evolutionary optimization search ability (2016)
  2. Koutsovasilis, Panagiotis; Driot, Nicolas; Lu, Daxining; Schweizer, Bernhard: Quantification of sub-synchronous vibrations for turbocharger rotors with full-floating ring bearings (2015)
  3. Astudillo, César A.; Oommen, B.John: Topology-oriented self-organizing maps: a survey (2014)
  4. Gracia, Antonio; González, Santiago; Robles, Victor; Menasalvas, Ernestina: A methodology to compare dimensionality reduction algorithms in terms of loss of quality (2014)
  5. Koc, Elcin Kartal; Iyigun, Cem: Restructuring forward step of MARS algorithm using a new knot selection procedure based on a mapping approach (2014)
  6. Koc, Elcin Kartal; Iyigun, Cem; Batmaz, İnci; Weber, Gerhard-Wilhelm: Efficient adaptive regression spline algorithms based on mapping approach with a case study on finance (2014)
  7. Koutsovasilis, P.; Schweizer, B.: Parameter variation and data mining of oil-film bearings: a stochastic study on the Reynolds’s equation of lubrication (2014)
  8. Ortiz, A.; Gorriz, J.M.; Ramirez, J.; Salas-Gonzalez, D.: Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering (2014)
  9. Vanderhaegen, Frédéric; Zieba, Stéphane: Reinforced learning systems based on merged and cumulative knowledge to predict human actions (2014)
  10. Cabanes, Guénaël; Bennani, Younès; Destenay, Renaud; Hardy, André: A new topological clustering algorithm for interval data (2013)
  11. Soulat, Laurent; Ferrand, Pascal; Moreau, Stéphane; Aubert, Stéphane; Buisson, Martin: Efficient optimisation procedure for design problems in fluid mechanics (2013)
  12. Caridakis, G.; Karpouzis, K.; Drosopoulos, A.; Kollias, S.: Non parametric, self organizing, scalable modeling of spatiotemporal inputs: the sign language paradigm (2012)
  13. Ortiz, A.; Gorriz, J.M.; Ramirez, J.; Salas-Gonzalez, D.: Unsupervised neural techniques applied to MR brain image segmentation (2012)
  14. Sassi, Renato José: An hybrid architecture for clusters analysis: rough sets theory and self-organizing map artificial neural network (2012)
  15. Sirola, Miki; Talonen, Jaakko: Combining neural methods and knowledge-based methods in accident management (2012)
  16. Hassan, Rohayanti; Othman, Razib M.; Saad, Puteh; Kasim, Shahreen: A compact hybrid feature vector for an accurate secondary structure prediction (2011)
  17. Kamimura, Ryotaro: Constrained information maximization by free energy minimization (2011)
  18. Kamimura, Ryotaro: Selective information enhancement learning for creating interpretable representations in competitive learning (2011)
  19. Kamimura, Ryotaro: Self-enhancement learning: target-creating learning and its application to self-organizing maps (2011)
  20. Cabanes, Guénaël; Bennani, Younès: Unsupervised topographic learning for spatiotemporal data mining (2010)

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