Titlebook: Advances in Self-Organising Maps; Nigel Allinson,Hujun Yin,Jon Slack Conference proceedings 2001 Springer-Verlag London Limited 2001 Data

斑駁

Optical Properties of Polytypes of Germaniumtion measure-based model represents each cluster. The proposed algorithm is iterative. At the end of each iteration, a competition between the models is performed. Then the data is regrouped between the models. The “movement” of the data between the models and the retraining allows the minimization

誘拐

Computational Solid State Physicsdom in the input vector are represented in the code. In particular, this may be applied to the problem of encoding data that predominantly lies on a manifold whose dimensionality is much less than the full dimensionality of the data space. In effect, the approach learns a subspace that approximates

ASTER

A Review of Solution Techniques,uctural information contained in geometrical structures is extracted using the pairwise geometric histograms. These histograms are quantised using a self-organising maps (SOM), as the SOMs offer a number of advantages over the standard equidistance histogram quantisation Using this trained SOM, a gl

gonioscopy

AUGER

General Equations for Planetary Flight,e expression of a set of yeast genes in several experimental treatments. The structures are visualized in an intuitive manner with colors: The similarity of hue correspond to the similarity of the multivariate data. The clusters can be interpreted by visualizing changes of the data variables (expres

STANT

INTER

Towards an information-theoretic approach to kernel-based topographic map formation,mize the differential entropies of the kernel outputs and, at the same time, to minimize the mutual information between these outputs. The learning scheme is based on . learning supplemented with a cooperative/competitive stage to achieve topographically-organized maps. As a potential application, we consider density estimation.

AWL

Collected

Blemish

Analysing Health Inequalities Using SOM,to identify health inequality structure is critical. It is believed that health inequality analysis is a complex problem. Self- organisation mapping is therefore employed in this paper to analyse health inequalities based on a data set provided by the Centre for Disease Control and Prevention, USA (CDC).