The kernel method has become a useful trick and has been widely applied to various learning models to extend their nonlinear approximation and classification capabilities. Such extensions have also recently occurred to the Self-Organising Map (SOM). In this paper, two recently proposed kernel SOMs are reviewed, together with their link to an energy function. The Self-Organising Mixture Network ...

Image classification is an important topic in digital image processing, and it could be solved by pattern recognition methods. This paper is a survey based on Self Organising Maps used as a supervised algorithm for image classification. It is observed that SOM can be used as a supervised method, and can have better advantages: better predictions, easier to interpret and better stability. Keywor...

Textual data plays an important role in the modern world. The possibilities of applying data mining techniques to uncover hidden information present in large volumes of text collections is immense. The Growing Self Organizing Map (GSOM) is a highly successful member of the Self Organising Map family and has been used as a clustering and visualisation tool across wide range of disciplines to dis...

This paper presents a novel method for enabling a robot to determine the position of a sound source in three dimensions using just two microphones and interaction with its environment. The method uses the Parameter-Less SelfOrganising Map (PLSOM) algorithm and Reinforcement Learning (RL) to achieve rapid, accurate response. We also introduce a method for directional filtering using the PLSOM. T...

Automatic clustering of documents is a task that has become increasingly important with the explosion of online information. The SelfOrganising Map (SOM) has been used to cluster documents effectively, but efforts to date have used a single or a series of 2-dimensional maps. Ideally, the output of a document-clustering algorithm should be easy for a user to interpret. This paper describes a met...

This paper presents an approach to the problem of automatically classifying events detected by video surveillance systems; specifically, of detecting unusual or suspicious movements. Approaches to this problem typically involve building complex 3D-models in real-world coordinates to provide trajectory information for the classifier. In this paper we show that analysis of trajectories may be car...

A feed-forward neural network is proposed for monitoring operating modes of large scale processes. A Gaussian hidden layer associated with a Kohonen output layer map the principal features of measurements of state variables. Subsets of selective neurons are generated into the hidden layer by means of self adapting of centers and dispersions parameters of the Gaussian functions. The output layer...

Statistical machine learning methods can provide help when developing preventative services and tools that support the empowerment of individuals. We explore how the self-organizing map could be utilized as a tool for analyzing, visualizing and browsing heterogeneous survey data on wellbeing that contains both quantitative (numeric) and qualitative (text) data. There is systematic evidence impl...