Self-organizing maps (SOM) are widely used for their topology preservation property: neighboring input vectors are quantified (or classified) either on the same location or on neighbor ones on a predefined grid. SOM are also widely used for their more classical vector quantization property. We show in this paper that using SOM instead of the more classical Simple Competitive Learning (SCL) algo...

In this paper we test the Self-Organizing Map (SOM) on the problem of predicting chaotic time-series (speciically Mackey-Glass series) with local linear models deened separately for each of the prototype vectors of the SOM. We see that the method achieves good results. This together with the capabilities of the SOM make it a valuable tool in exploratory data mining.

När en cell utsätts för strålning startar en signalkaskad som kan aktivera DNA-reparation, hämma celldelning och påverka cellens benägenhet att dö. En sådan signalväg är HER2/fosfatidylinositol 3-kinas (PI3K)/AKT, som bland annat reglerar celltillväxt, celldelning och en form av celldöd som kallas apoptos. HER2 är uttryckt i onormalt höga nivåer i 1530% av alla brösttumörer och är relaterat til...

In this paper we test the Self-Organizing Map (SOM) on the problem of predicting chaotic time-series (speci cally Mackey-Glass series) with local linear models de ned separately for each of the prototype vectors of the SOM. We see that the method achieves good results. This together with the capabilities of the SOM make it a valuable tool in exploratory data mining.

A method for discrimination and classification of breast cancer dataset with benign and malignant tissues is proposed using Independent Component Analysis (ICA) and Self Organizing Map (SOM). The method implement ICA for preprocessing and data reduction and SOM for data analysis. The best performance was obtained with ICASOM, resulting in 98.8% classification accuracy and a SOM result is 94.9%.

Clustering analysis methods have been widely applied to identifying the functional brain networks of a multitask paradigm. However, the previously used clustering analysis techniques are computationally expensive and thus impractical for clinical applications. In this study a novel method, called SOM-SAPC that combines self-organizing mapping (SOM) and supervised affinity propagation clustering...

In this paper, we proposed a hybrid system to combine the self-organizing map (SOM) of neural network with case-based reasoning (CBR) method, for sales forecast of new released books. CBR systems have been successfully used in several domains of artificial intelligence. In order to enhance efficiency and capability of CBR systems, we connected the SOM method to deal with cluster problems of CBR...

In order to control a prostheses by means of biological nerve signals, a self-organizing map (SOM) has been used to classify nerve signals recorded by a regeneration type neurosensor. The trained SOM contains the information about the relation between the recorded nerve signal and the winning neuron of the SOM. Classes of nerve signals red by de ned axons can be found in cluster on the SOM. For...

Self-organizing map (SOM) has been studied as a model of map formation in the brain cortex. However, the original model present several oversimplifications. For example, neurons in the cortex present a refractory period in which they are not able to be activated, restriction that should be included in the SOM if a better model is to be achieved. Although several modifications have been studied ...

We propose a new method called C-SOM using a Self-Organizing Map (SOM) for function approximation. C-SOM takes care about the output values of the «win-ning» neuron's neighbors of the map to compute the output value associated with the input data. Our work extends the standard SOM with a combination of Local Linear Mapping (LLM) and cubic spline based interpolation techniques to improve its gen...