Self-Organizing Maps, or Kohonen networks, are a widely used neural network architecture. This paper starts with a brief overview of how selforganizing maps can be used in different types of problems. A simple and intuitive explanation of how a self-organizing map is trained is provided, together with a formal explanation of the algorithm, and some of the more important parameters are discussed...

In this paper a description of business process simulation is given. A crucial part in the simulation of business processes is the analysis of social contacts between the participants. We will introduce a tool to collect log data and how this log data can be eeectively analyzed using two diierent kind of methods: discussion ow charts and self-organizing maps. Discussion ow charts revealed the c...

Neighborhood algorithm is an important part of 3D SOM algorithm. We proposed three kinds of neighborhood shape and two kinds of neighborhood decay function for threedimensional self-organizing feature maps (3D SOM) algorithm and applied them to three-dimensional image compression coding. Experimental results show that the 3D orthogonal cross neighborhood shape and exponential function algorithm...

We report on the results of a pilot study in which a data-mining tool was developed for mining audiology records. The records were heterogeneous in that they contained numeric, category and textual data. The tools developed are designed to observe associations between any field in the records and any other field. The techniques employed were the statistical chi-squared test, and the use of self...

The paper presents the design of the parameters for long-term municipal rating. Modelling of the rating is realized by means of unsupervised methods, because the rating classes are not known a priori. The model design based on statistical methods (neural networks) is represented by cluster analysis (self-organizing feature maps). Key-Words: Credit risk, rating, unsupervised learning, cluster an...

Collaboration of growing self-organizing maps (GSOM) and adaptive resonance theory maps (ART) is considered through traveling sales-person problems (TSP).The ART is used to parallelize the GSOM: it divides the input space of city positions into subspaces automatically. One GSOM is allocated to each subspace and grows following the input data. After all the GSOMs grow sufficiently they are conne...

This paper shows that a system with two link arm can obtain arm reaching movement to a target object by combination of reinforcement learning and dynamic self organizing map. Proposed model in this paper present state and action space of reinforcement learning with dynamis self organizing maps. Because these spaces are continuous. proposed model uses two dynamic self-organizing maps (DSOM) to e...

Three different auditory representations—Lyon’s cochlear model, Patterson’s gammatone filterbank combined with Meddis’ inner hair cell model, and mel-frequency cepstral coefficients—are analyzed in connection with self-organizing maps to evaluate their suitability for a perceptually justified classification of sounds. The self-organizing maps are trained with a uniform set of test sounds prepro...

Despite much theoretical support, meta-analysis of the efficacy of formative assessment does not provided empirical evidence commensurate with expectations. This theoretical study suggests that teachers need a better organizing structure to allow a formative assessment process to live up to its promise. We propose that the use of learning map systems can provide that structure, and we describe ...

We present an expectation-maximization (EM) algorithm that yields topology preserving maps of data based on probabilistic mixture models. Our approach is applicable to any mixture model for which we have a normal EM algorithm. Compared to other mixture model approaches to self-organizing maps, the function our algorithm maximizes has a clear interpretation: it sums data log-likelihood and a pen...