Kalman filter implementation of self-organizing feature maps
Neural Computation
Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Self-Organizing Maps
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4 - Volume 4
Context-Aware Visual Exploration of Molecular Datab
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Self-organizing feature maps with self-adjusting learning parameters
IEEE Transactions on Neural Networks
The parameterless self-organizing map algorithm
IEEE Transactions on Neural Networks
A comparison between habituation and conscience mechanism in self-organizing maps
IEEE Transactions on Neural Networks
Clustering Quality and Topology Preservation in Fast Learning SOMs
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
A New Linear Initialization in SOM for Biomolecular Data
Computational Intelligence Methods for Bioinformatics and Biostatistics
Self-Organising maps for classification with metropolis-hastings algorithm for supervision
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
A New Training Method for Large Self Organizing Maps
Neural Processing Letters
| Hi-index | 0.00 |
Self-Organizing Map (SOM) algorithm has been extensively used for analysis and classification problems. For this kind of problems, datasets become more and more large and it is necessary to speed up the SOM learning. In this paper we present an application of the Simulated Annealing (SA) procedure to the SOM learning algorithm. The goal of the algorithm is to obtain fast learning and better performance in terms of matching of input data and regularity of the obtained map. An advantage of the proposed technique is that it preserves the simplicity of the basic algorithm. Several tests, carried out on different large datasets, demonstrate the effectiveness of the proposed algorithm in comparison with the original SOM and with some of its modification introduced to speed-up the learning.