In Self-Organizing Maps (SOM) are unsupervised neural networks that cluster high dimensional data and transform complex inputs into easily understandable inputs. To find the closest distance weight factor, it maps input space to low space. The Closest node point is denoted as a neuron. It classifies based on these neurons. reduction of dimensionality grid clustering using neurons makes observe ...

Organizing Lobar Pneumonia is a rare form of Bronchiolitis Obliterans Organizing Pneumonia.  Herein, we report a rare case of organizing pneumonia involving lung, mediastinum and esophagus.  A 16-year-old girl was referred to our center with clinical signs and symptoms of dysphagia and weight loss.  The main abnormal radiologic and endoscopic findings were stricture of the lower third of esopha...

In this paper we describe an e4ort to project an olfactory perception database onto the nearest high dimensional Euclidean space using multidimensional scaling. This yields an independent Euclidean interpretation of odor perception, whether this space is metric or not. Self-organizing maps were then applied to produce two-dimensional maps of the Euclidean approximation of olfactory perception s...

Based on principles found in biological systems a neural network model is proposed that nds a robust and adaptive representation of signal space. Self-organizing sub-networks analyze diierent signal properties. Local dynamics are used for signal-driven adaptation of network parameters.

It has been 17 years since the first Workshop on Self-organizing Maps (WSOM) was held in Helsinki, Finland in 1997, under the leadership of Teuvo Kohonen. The workshop brings together researchers and practitioners in the field of self-organizing systems and related areas. The 9th WSOMwas held for the first time in LatinAmerica, at the Universidad de Chile, Santiago, Chile, on December 2012. Thi...

This paper introduces a hierarchical Self-Organizing Feature Map (SOFM). The partial maps consist of individual numbers of neurons, which makes a cluster analysis with di erent degrees of resolution possible. A de nition of a special Mahalanobis space of the data set during the learning improves the properties concerning clusters with low density.

The Self Organizing Map (SOM) proposed by Kohonen [7] is a well known neural model which provides both quantization and clustering of the observation space. In this paper, we adapt the Bernoulli mixture approach, proposed by [6], to the model of binary topological map [2] and show that using a probabilistic formalism gives rise to better quantization process and classi cation performances.