SOMMER is a publicly available, Java-based toolbox for training and visualizing two- and three-dimensional unsupervised self-organizing maps (SOMs). Various map topologies are implemented for planar rectangular, toroidal, cubic-surface and spherical projections. The software allows for visualization of the training process, which has been shown to be particularly valuable for teaching purposes.

This paper presents a taxonomy for Self-organizing Maps (SOMs) for temporal sequence processing. Four main application areas for SOMs with temporal processing have been identified. These are prediction, control, monitoring and data mining. Three main techniques have been used to model temporal relations in SOMs: 1) pre-processing or post-processing the data, but keeping the basic SOM algorithm;...

Kohonen neural nets are some kind of competitive nets. The most commonly known variants are the Self-Organizing Maps (SOMs) and the Learning Vector Quantization (LVQ). The former model uses an unsupervized learning, the latter is an e cient classi er. This paper tries to give, in simple words, a clear idea about the basis of competitive neural nets and competitive learning emphasizing on the SO...

We have developed an image retrieval system named Pic-SOM which uses Tree Structured Self-Organizing Maps (TS-SOMs) as the method for retrieving images similar to a given set of reference images. A novel technique introduced in the PicSOM system facilitates automatic combination of the responses from multiple TS-SOMs and their hierarchical levels. This mechanism aims at adapting to the user's p...

Self-Organizing Maps (SOMs, [1,2]) are well known in the domain of Vector Quantization (VQ). Unlike other VQ methods, the neurons (or prototypes) used for the quantization are given a position in a grid, which is often oneor two-dimensional. This predefined geometrical organization, combined with a well chosen learning rule, generates a self-organizing behavior, useful in numerous areas like no...

Self-organizing maps, SOMs, are a data visualization technique developed to reduce the dimensions of data through the use of self-organizing neural networks. However, as the original input manifold can be complicated with an inherent dimension larger than that of the feature map, the dimension reduction in SOM can be too drastic, generating a folded feature map. In order to eliminate this pheno...

Self Organizing Maps (SOMs) have attracted the attention of many computer vision scientists, particularly when dealing with image segmentation as a contour extraction problem. The idea of utilizing the prototypes (weights) of a SOM to model an evolving contour has produced a new class of Active Contour Models (ACM s), known as SOM based ACM s. Such models have been proposed in general with the ...

Self-organizing maps (SOMs, Kohonen, 1982) have been extensively used as information processing models in music perception. Using a corpus that represents tonal regularities in Western music, Tillmann et.al (2000) developed a schema of Western tonal music. Krumhansl, et.al (2000) showed that melodic expectancy in Finnish music could be modeled by SOMs trained on samples of Finnish music. Recent...

A Self-Organizing Map (SOM) is a type of artificial neural network used to transform a data set of vectors into a set of lower dimensional vectors. The applications of this are wider than one might expect, but this is the heart of its purpose. The data are usually some set of vectors {x ∈ Rn}, but a SOM can work with any set of vectors that have a well-defined distance metric. The canonical exa...