Add to ORKGapplication/pdfSelf Organizing Map is trained using unsupervised learning to produce a two dimensional discretized representation of input space of the training cases. Growing Hierarchical SOM is an architecture which grows both in a hierarchical representation for the structure of data distribution, and in a horizontal representation for the size of each individual maps. The control method of the growing degree of GHSOM was proposed by pruning off the redundant branch of its hierarchy. In this paper, the interface tool for the proposed method called interactive GHSOM is developed. We discuss the computational results of Iris data by using the developed tool.開催日：平成23年7月9日 会場：広島市立大
This paper proposes an extension of the self-organizing map (SOM), in which the mapping objects them...
Abstract — The Self-Organizing Map (SOM) is popular algo-rithm for unsupervised learning introduced ...
Abstract — Based on the principles of the self-organizing map, we have designed a novel neural net-w...
application/pdfSelf－Organizing Maps trained using unsupervised learning to produce a two－dimensional...
A self organizing map (SOM) is trained using unsupervised learning to produce a twodimensional discr...
In this paper we introduce a tree structured self-organizing network, called the Growing Hierarchica...
This work presents a neural network model for the clustering analysis of data based on Self Organizi...
Abstract. The Growing Hierarchical Self Organizing Map (GHSOM) was introduced as a dynamical neural ...
The Self-Organizing Map is a very popular unsupervised neural network model for the analysis of high...
We propose a hierarchical self-organizing neural network ( HiGS ) with adaptive architecture and sim...
Abstract:- The Self-Organizing Map (SOM) has shown to be a stable neural network model for high- dim...
AbstractThe use of self-organising maps (SOM) in unsupervised knowledge discovery has been successfu...
This paper introduces a comprehensive review of a Growing Hierarchical Self-Organizing Map (GHSOM) r...
Self-organizing maps (SOMs) have become popular for tasks in data visualization, pattern classificat...
We present an algorithm, Hierarchical ISOmetric Self-Organizing Map (H-ISOSOM), for a concise, organ...
This paper proposes an extension of the self-organizing map (SOM), in which the mapping objects them...
Abstract — The Self-Organizing Map (SOM) is popular algo-rithm for unsupervised learning introduced ...
Abstract — Based on the principles of the self-organizing map, we have designed a novel neural net-w...
application/pdfSelf－Organizing Maps trained using unsupervised learning to produce a two－dimensional...
A self organizing map (SOM) is trained using unsupervised learning to produce a twodimensional discr...
In this paper we introduce a tree structured self-organizing network, called the Growing Hierarchica...
This work presents a neural network model for the clustering analysis of data based on Self Organizi...
Abstract. The Growing Hierarchical Self Organizing Map (GHSOM) was introduced as a dynamical neural ...
The Self-Organizing Map is a very popular unsupervised neural network model for the analysis of high...
We propose a hierarchical self-organizing neural network ( HiGS ) with adaptive architecture and sim...
Abstract:- The Self-Organizing Map (SOM) has shown to be a stable neural network model for high- dim...
AbstractThe use of self-organising maps (SOM) in unsupervised knowledge discovery has been successfu...
This paper introduces a comprehensive review of a Growing Hierarchical Self-Organizing Map (GHSOM) r...
Self-organizing maps (SOMs) have become popular for tasks in data visualization, pattern classificat...
We present an algorithm, Hierarchical ISOmetric Self-Organizing Map (H-ISOSOM), for a concise, organ...
This paper proposes an extension of the self-organizing map (SOM), in which the mapping objects them...
Abstract — The Self-Organizing Map (SOM) is popular algo-rithm for unsupervised learning introduced ...
Abstract — Based on the principles of the self-organizing map, we have designed a novel neural net-w...