Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Vehicle Segmentation and Classification Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the accuracy of binned kernel density estimators
Journal of Multivariate Analysis
Self-organizing maps
Kernel-based equiprobabilistic topographic map formation
Neural Computation
The Self-organizing map as a tool in knowledge engineering
Pattern recognition in soft computing paradigm
Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Visual Explorations in Finance
Visual Explorations in Finance
Journal of VLSI Signal Processing Systems
Silhouette-Based Isolated Object Recognition through Curvature Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel-based topographic map formation by local density modeling
Neural Computation
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Asymptotic level density in topological feature maps
IEEE Transactions on Neural Networks
Guest Editorial for Special Issue on Machine Learning for Signal Processing
Journal of VLSI Signal Processing Systems
A hybrid SOM-kMER model for data visualization and classification
International Journal of Hybrid Intelligent Systems
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A crucial issue when applying topographic maps for clustering purposes is how to select the map's overall degree of smoothness. In this paper, we develop a new strategy for optimally smoothing, by a common scale factor, the density estimates generated by Gaussian kernel-based topographic maps. We also introduce a new representation structure for images of shapes, and a new metric for clustering them. These elements are incorporated into a hierarchical, density-based clustering procedure. As an application, we consider the clustering of shapes of marine animals taken from the SQUID image database. The results are compared to those obtained with the CSS retrieval system developed by Mokhtarian and co-workers, and with the more familiar Euclidean distance-based clustering metric.