Dynamic cell structure learns perfectly topology preserving map
Neural Computation
Self-organizing maps
PicSOM—content-based image retrieval with self-organizing maps
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
ACM Computing Surveys (CSUR)
Defect image classification and retrieval with MPEG-7 descriptors
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
IEEE Transactions on Circuits and Systems for Video Technology
PicSOM-self-organizing image retrieval with MPEG-7 content descriptors
IEEE Transactions on Neural Networks
Large-scale data exploration with the hierarchically growing hyperbolic SOM
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
An application of one-class support vector machines in content-based image retrieval
Expert Systems with Applications: An International Journal
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Hierarchical PCA Using Tree-SOM for the Identification of Bacteria
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Clustering Bacteria Species Using Neural Gas: Preliminary Study
Computational Intelligence Methods for Bioinformatics and Biostatistics
A Framework for Designing a Fuzzy Rule-Based Classifier
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
On Using Adaptive Binary Search Trees to Enhance Self Organizing Maps
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Evolving tree algorithm modifications
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Content-based image retrieval by combining genetic algorithm and support vector machine
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
On cluster tree for nested and multi-density data clustering
Pattern Recognition
MIForests: multiple-instance learning with randomized trees
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
On-line multi-view forests for tracking
Proceedings of the 32nd DAGM conference on Pattern recognition
Advanced visualization techniques for self-organizing maps with graph-based methods
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Binary tree time adaptive self-organizing map
Neurocomputing
Analyzing large image databases with the evolving tree
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
On achieving semi-supervised pattern recognition by utilizing tree-based SOMs
Pattern Recognition
| Hi-index | 0.00 |
The Self-Organizing Map (SOM) is one of the best known and most popular neural network-based data analysis tools. Many variants of the SOM have been proposed, like the Neural Gas by Martinetz and Schulten, the Growing Cell Structures by Fritzke, and the Tree-Structured SOM by Koikkalainen and Oja. The purpose of such variants is either to make a more flexible topology, suitable for complex data analysis problems or to reduce the computational requirements of the SOM, especially the time-consuming search for the best-matching unit in large maps. We propose here a new variant called the Evolving Tree which tries to combine both of these advantages. The nodes are arranged in a tree topology that is allowed to grow when any given branch receives a lot of hits from the training vectors. The search for the best matching unit and its neighbors is conducted along the tree and is therefore very efficient. A comparison experiment with high dimensional real world data shows that the performance of the proposed method is better than some classical variants of SOM.