Add to ORKGAbstract — 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
Abstract: A neural-network-based data-analysis model for the prediction and classification of field ...
In this paper, we apply the combination method of bagging which has been developed in the context of...
Abstract — Support Vector Machines (SVMs) are a relatively new supervised classification technique t...
A self-organizing map (SOM) for processing of structured data, using an unsupervised learning approa...
International audienceWe present in this paper a new approach of supervised self organizing map (SOM...
Abstract. Self-Organising Maps (SOM) provide a method of feature mapping from multi-dimensional spac...
The Self-Organizing Map (SOM) is a neural network algorithm, which uses a competitive learning techn...
In this paper the basic principles and developments of an unsupervised learning algorithm, the Self-...
The Self-Organizing Maps (SOM) is a very popular algorithm, introduced by Teuvo Kohonen in the early...
Kohonen Self Organizing Maps (SOM) has found application in practical all fields, especially those w...
Learning in self-organizing maps (SOM) is considered unsupervised because training patterns do not n...
Self-organizing maps (SOMs) were developed by Teuvo Kohonen in the early eighties. Since then more t...
Pattern recognition is the science which helps in getting inferences from input data, usage of tools...
Self-Organizing Maps (SOMs) have been useful in gaining insights about the information content of la...
Image segmentation is an essential step in image processing. Many image segmentation methods are ava...
Abstract: A neural-network-based data-analysis model for the prediction and classification of field ...
In this paper, we apply the combination method of bagging which has been developed in the context of...
Abstract — Support Vector Machines (SVMs) are a relatively new supervised classification technique t...
A self-organizing map (SOM) for processing of structured data, using an unsupervised learning approa...
International audienceWe present in this paper a new approach of supervised self organizing map (SOM...
Abstract. Self-Organising Maps (SOM) provide a method of feature mapping from multi-dimensional spac...
The Self-Organizing Map (SOM) is a neural network algorithm, which uses a competitive learning techn...
In this paper the basic principles and developments of an unsupervised learning algorithm, the Self-...
The Self-Organizing Maps (SOM) is a very popular algorithm, introduced by Teuvo Kohonen in the early...
Kohonen Self Organizing Maps (SOM) has found application in practical all fields, especially those w...
Learning in self-organizing maps (SOM) is considered unsupervised because training patterns do not n...
Self-organizing maps (SOMs) were developed by Teuvo Kohonen in the early eighties. Since then more t...
Pattern recognition is the science which helps in getting inferences from input data, usage of tools...
Self-Organizing Maps (SOMs) have been useful in gaining insights about the information content of la...
Image segmentation is an essential step in image processing. Many image segmentation methods are ava...
Abstract: A neural-network-based data-analysis model for the prediction and classification of field ...
In this paper, we apply the combination method of bagging which has been developed in the context of...
Abstract — Support Vector Machines (SVMs) are a relatively new supervised classification technique t...