By Nigel Allinson
This is the 3rd Workshop on Self-Organising Maps (WSOM) and its similar suggestions. the former have been held in Helsinki (1997 and 1999) and proven the power of the SOM as some of the most well known and robust thoughts for unsupervised trend reputation and information visualisation. those conferences not just acted as a exhibit for the most recent advances in SOM thought and for illustrating its huge variety of applicability, but in addition as venues the place a lot casual and fruitful interplay may possibly ensue. it really is attention-grabbing to monitor the advance of the unique SOM, and this awesome development confrrms the originality and perception of Teuvo Kohonen's pioneering paintings. With the diversity and caliber of the papers during this quantity, the level is determined for an additional very winning assembly. This quantity is an enduring checklist of all of the contributions offered in the course of WSOM'OI held on the college of Lincolnshire and Humberside, thirteen - 15 June, 2001. The collage is the most recent of England's universities however it is positioned within the center of 1 of our oldest towns - based by means of the Romans and missed through the towering mass of its medieval cathedral. basically Lincoln has constantly been a centre for the wealthy agricultural heartland of britain; despite the fact that, it was once the birthplace, 186 years in the past, of George Boole. So WSOM'OI is constant Lincoln's lengthy and honourable culture of advancing clinical understanding.
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Three weeks to train a 10 x 15 map with 70 input patterns, 160 features each on a Sun Ultra5). Results are illustrated by two plots, one showing the map after applying UMM algorithm for visualisation [11] to show the different clusters (shown in dark colour) separated by the borders (in bright colour). The vertical axis in the UMM plot represents the average distance between each node and its neighbourhood nodes. The threshold set to discriminate the different clusters depends on the UMM values for every experiment and a quiver and a contour diagram of the map showing the direction of attraction for the different regions.
It proved to be convenient because it maintains the temporal dynamics of the motion signal. Furthermore, it analyses the signal on an adaptable time-scale. The scalograms produced were split into different levels - high, medium and low scale from which feature vectors 37 were selected. A Kohonen SOM was used as the vector quantiser required for classification for two main reasons, visualisation and rule extraction. Different experiments were carried out to investigate the walking signatures in normal and pathological subjects using different levels of scale.
The wavelet transform is implemented to extract spatio-temporal features in kinematic data . • Recognition: using the spatio-temporal feature vectors of different subjects,a vector quantiser, namely a Kohonen self-organising map (SaM), is used to classify the different walking profiles into clusters based on the internal structure of the feature vectors. A set of experiments is carried 32 out splitting feature vectors into low and high-scale feature vectors and another set splitting them into low, medium and high-scale features.