Published by InTech. Janeza Trdine 9, 51000 Rijeka, Croatia. 2010.
ISBN 978-953-307-074-2, Hard cover, 430 p.
Первоисточник: http://www.intechopen.com.
Contents:
An adaptive fuzzy neural network based on self-organizing map (som)
Leaing the number of clusters in self organizing map
Improvements quality of Kohonen maps using dimension reduction methods
Partsom: a framework for distributed data clustering using SOM and K-MEANS
Kohonen maps combined to k-means in a two level strategy for time series clusteringapplication to meteorological and electricity load data
Visual-interactive analysis with self-organizing maps - advances and research
Tracking and visualization of cluster dynamics by sequence-based SOM
Visualization with Voronoi tessellation and moving output units in self-organizing map of the real-number system
Using self organizing maps for 3d surface and volume adaptive mesh generation
Neural-network enhanced visualization of high-dimensional data
The self-organizing approach for surface reconstruction from unstructured point clouds
Self-organizing maps for processing of data with missing values and outliers: application to remote sensing images
Image clustering and evaluation on impact perforation test by self-organizing map
Self-organizing map-based applications in remote sensing
Segmentation of satellite images using self-organizing maps
Bridging the semantic gap using human vision system inspired features
Face recognition using self-organizing maps
Generation of emotional feature space for facial expression recognition using self-mapping
Fingerprint matching with self organizing maps
Multiple self-organizing maps for control of a redundant manipulator with multiple cameras
Tracking English and translated Arabic news using GHSOM
Self-organizing maps in web mining and semantic web
Secure wireless mesh network based on human immune system and self-organizing map
A knowledge acquisition method of judgment rules for spam e-mail by using self organizing map and automatically defined groups by genetic programming
Applying an SOM neural network to increase the lifetime of battery-operated wireless sensor networks
Первоисточник: http://www.intechopen.com.
Contents:
An adaptive fuzzy neural network based on self-organizing map (som)
Leaing the number of clusters in self organizing map
Improvements quality of Kohonen maps using dimension reduction methods
Partsom: a framework for distributed data clustering using SOM and K-MEANS
Kohonen maps combined to k-means in a two level strategy for time series clusteringapplication to meteorological and electricity load data
Visual-interactive analysis with self-organizing maps - advances and research
Tracking and visualization of cluster dynamics by sequence-based SOM
Visualization with Voronoi tessellation and moving output units in self-organizing map of the real-number system
Using self organizing maps for 3d surface and volume adaptive mesh generation
Neural-network enhanced visualization of high-dimensional data
The self-organizing approach for surface reconstruction from unstructured point clouds
Self-organizing maps for processing of data with missing values and outliers: application to remote sensing images
Image clustering and evaluation on impact perforation test by self-organizing map
Self-organizing map-based applications in remote sensing
Segmentation of satellite images using self-organizing maps
Bridging the semantic gap using human vision system inspired features
Face recognition using self-organizing maps
Generation of emotional feature space for facial expression recognition using self-mapping
Fingerprint matching with self organizing maps
Multiple self-organizing maps for control of a redundant manipulator with multiple cameras
Tracking English and translated Arabic news using GHSOM
Self-organizing maps in web mining and semantic web
Secure wireless mesh network based on human immune system and self-organizing map
A knowledge acquisition method of judgment rules for spam e-mail by using self organizing map and automatically defined groups by genetic programming
Applying an SOM neural network to increase the lifetime of battery-operated wireless sensor networks