Advances in Self-Organising Maps

Herausgegeben von Allinson, Nigel; Yin, Hujun; Allinson, Lesley; Slack, Jon

• Broschiertes Buch

Produktbeschreibung

The Self-Organising Map (SOM), with its related techniques, is one of the most popular and powerful concepts for unsupervised pattern recognition and data visualisation. Over 3,000 applications have been reported in the open literature, and many commercial projects employ the SOM as the tool for tackling real-world problems.This volume is a complete record of the Third Workshop on Self-Organising Maps, which brought together the leading international researchers and users for an intensive three-day meeting in Lincoln, UK, and so represents the very latest developments in both theory and application of SOMs and associated approaches.

Topics covered include:

- Pattern recognition and data clustering

- Unsupervised learning using SOMs and other techniques

- Data visualisation

- Commercial and financial applications

- Multi-dimensional signal processing

- Component and system design and condition monitoring

Produktdetails

• Verlag: Springer / Springer London / Springer, Berlin
• Artikelnr. des Verlages: 978-1-85233-511-3
• 1st Edition.
• Seitenzahl: 304
• Erscheinungstermin: 18. Juni 2001
• Englisch
• Abmessung: 235mm x 155mm x 17mm
• Gewicht: 460g
• ISBN-13: 9781852335113
• ISBN-10: 1852335114
• Artikelnr.: 22430501

Inhaltsangabe

Towards an information-theoretic approach to kernel-based topographic map formation.-A statistical tool to assess the reliability of self-organising maps.-A SOM association network.-A supervised self-organising map for structured data.-Human gait analysis using SOM.-Exploring financial crises data with self-organising maps (SOM).-Analysing health inequalities using SOM.-Integrating contextual information into text document clustering with self-organising maps.-Self-organising Internet semantic network.-Recursive learning rules for SOMs.-Induced Vorono kernels for principal manifolds approximation.-Visualisation induced SOM (ViSOM).-An approach to automated interpretation of SOM.-VQ-based clustering algorithm of piecewise-dependant-data.-Adaptive subspace encoders using stochastic vector quantisers.-SHAPESOM.-A new interpolation algorithm employing a self organising map.-SOM-based exploratory analysis of gene expression data.-Exploring power transformer database using self-organising maps (SOM) and minimal spanning tree (MST).-Recent advances with the growing hierarchical self-organising Map.-Self-organising maps of web link information.-A design method of DNA chips using self-organising maps.-Multi-dimensional self-organising maps on massively parallel hardware.-A new method of Hough transform by using SOM with input vector transformation.-Estimating relevant input dimensions for self-organising algorithms.- An overview on unsupervised learning from data mining perspective.-Recursive self-organising maps.-An investigation into catastrophic interference on a SOM network.-A topography-preserving latent variable model with learning metrics.-Vector quantisation with g-observable neighbors.-Dynamic vector quantisation of speech.-Optimisation of electronic parts mounting machines using SOM-TSP method with 5 dimensional data.-Self-organising maps forcondition assessment of paper insulated cables.-An essay in classifying self-organising maps for temporal sequence processing.-Evaluating SOM-based models in text classification tasks for the Greek language.-Nonlinear blind source separation using SOMs and applications to image denoising.-Signal-based feature extraction and SOM based dimension reduction in a vibration monitoring microsystem.