Feature Map Architectures for Pattern Recognition: Techniques for Automatic Region Selection

Martin-del-Brio, B.; Marques, Nicolas; Blasco-Alberto, Javier

doi:10.1007/978-3-7091-7535-4_34

Cited by 8 publications

(3 citation statements)

References 2 publications

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“…Other alternatives can be found in Flexer (2001), Kiang (2001) and Vesanto and Alhoniemi, 2000. A comprised approach is the twolevel SOM neural network which augments the conventional SOM network by an additional one-dimensional Kohonen layer in which each neuron is connected to the ones in the previous Kohonen layer (Li, 2002;Martín--del-Brío & Medrano, 1995). Fig.…”

Section: Self-organizing Map Neural Networkmentioning

confidence: 98%

Data mining to aid policy making in air pollution management

Shue

2004

Expert Systems with Applications

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Section: Self-organizing Map Neural Networkmentioning

confidence: 98%

Data mining to aid policy making in air pollution management

Shue

2004

Expert Systems with Applications

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“…This technique has been used in analyzing the financial condition of commercial banks, and has been shown to accurately determine whether at a given time-point a subject bank is profitable or in danger of bankruptcy. 6 A similar approach has been used to analyze data describing the medical condition of children, where the SOM has been shown to map a set of two-dimensional EEG signals so that a number of important constituents of the signal are uncovered. …”

Section: Som and The Cardiac Datamentioning

confidence: 99%

“…It might be argued, however, that a partiallycorrect analysis of the existing data regarding a subject is preferable to discarding the data, even though the results might be slightly inferior to those obtained with complete data. As the data have been accumulated over a long period of time (in excess of two years), different sampling frequencies (that is, different values of n) have been used, these being equal to 6,8,10,15, and 20 minutes.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the 24-hour blood pressure and heart-rate variability with self-organizing feature maps

Tambouratzis

Παπακωνσταντίνου

Stamatelopoulos

et al. 2002

Int. J. Intell. Syst.

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In this article, the self-organizing map (SOM) is employed to analyze data describing the 24-hour blood pressure and heart-rate variability of human subjects. The number of observations varies widely over different subjects, and therefore a direct statistical analysis of the data is not feasible without extensive pre-processing and interpolation for normalization purposes. The SOM network operates directly on the data set, without any pre-processing, determines several important data set characteristics, and allows their visualization on a two-dimensional plot. The SOM results are very similar to those obtained using classic statistical methods, indicating the effectiveness of the SOM method in accurately extracting the main characteristics from the data set and displaying them in a readily understandable manner. In this article, the relation is studied between the representation of each subject on the SOM, and his blood pressure and pulse-rate measurements. Finally, some indications are included regarding how the SOM can be used by the medical community to assist in diagnosis tasks.

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