This paper presents a multi-classifier system design controlled by the topology of the learning data. Our work also introduces a training algorithm for an incremental self-organizing map (SOM). This SOM is used to distribute classification tasks to a set of classifiers. Thus, the useful classifiers are activated when new data arrives. Comparative results are given for synthetic problems, for an...

The self-organising map (SOM) and its variant, visualisation induced SOM (ViSOM), have been known to yield similar results to multidimensional scaling (MDS). However, the exact connection has not been established. In this paper, a review on the SOM and its cost function and topological measures is provided first. We then examine the exact scaling effect of the SOM and ViSOM from their objective...

The paper describes a combinatorial creativity module embedded in a cognitive architecture. The proposed module is based on the focus of attention model proposed by and is implemented using Self Organising Map (SOM) neural networks.

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In this paper, we propose a proficient method for knowledge management in Edaphology using self organizing map (SOM). The method will assist the edaphologists and those related with agriculture in a big way by finding out the plants apt for the input query. The method has three phases namely dataset processing, neuron training and testing phase. The input data is first converted and normalized ...

A hexagonal Self-Organising Map (SOM) based on the Vulture Fest Model of intrusion detection illustrates the network landscape of wireless traffic comparing it with malicious traffic and contrasting it to traditional landline network traffic. This map can also provide visual security profiles of work groups and LAN administrators.

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.

Self-Organizing Map (SOM) is an unsupervised learning neural network and it is used for preserving the structural relationships in the data without prior knowledge. SOM has been applied in the study of complex problems such as vector quantizations, combinatorial optimization, and pattern recognition. This paper proposes a new usage of SOM as a tool for schema transformation hoping to achieve mo...

DNA microarray technologies together with rapidly increasing genomic sequence information is leading to an explosion in available gene expression data. Currently there is a great need for efficient methods to analyze and visualize these massive data sets. A self-organizing map (SOM) is an unsupervised neural network learning algorithm which has been successfully used for the analysis and organi...

Geometric independent component analysis (ICA) uses a weight update rule that is very similar to the self-organizing map (SOM) learning rule in the case of a trivial neighborhood function. In this paper we use this fact and present a new geometric ICA algorithm that uses a SOM for learning. The separation qualiry is better in comparison to other geometric algorithms, but the computational cost ...