A Maximum-likelihood connectionist model for unsupervised learning over graphical domains

The self-organizing map algorithm has been used successfully in document organization. We now propose using the same algorithm for document retrieval. Moreover, we test the performance of the self-organizing map by replacing the linear Least Mean Squares adaptation rule with the marginal median. We present two implementations of the latter variant of the self-organizing map by either quantifyin...

-This paper proposes and describes a hierarchical self-organizing neural network for range image segmentation. The multilayer self-organizing feature map (MLSOFM), which is an extension of the traditional (singlelayer ) self-organizing feature map ( SOFM) is seen to alleviate the shortcomings of the latter in the context of range image segmentation. The problem of range image segmentation is fo...

[1] Despite its wide applications as a tool for feature extraction, the Self-Organizing Map (SOM) remains a black box to most meteorologists and oceanographers. This paper evaluates the feature extraction performance of the SOM by using artificial data representative of known patterns. The SOM is shown to extract the patterns of a linear progressive sine wave. Sensitivity studies are performed ...

This paper presents a biologically inspired method for the navigation of autonomous mobile systems. The method calculates the way from a current position to a target position using one-dimensional 360° images, taken at these positions. The correlations between the two images are generated by using a modified version of Kohonen’s self-organizing feature map. The direction to the target position ...

The Self-Organizing Map has been successfully applied in numerous industrial applications. An important task in data analysis is finding and visualizing multiple dependencies in data. In this paper, we propose a method for visualizing the Self-Organizing Map by decomposing the feature dimensions into groups with high correlation or selections by domain experts. Using Gradient Visualization we p...

| A biologically motivated mechanism for self-organizing a neural network with modi able lateral connections is presented. The weight modi cation rules are purely activity-dependent, unsupervised and local. The lateral interaction weights are initially random but develop into a \Mexican hat" shape around each neuron. At the same time, the external inputweights self-organize to form a topologica...

An extension of a recently proposed evolutionary selforganizing map is introduced and applied to the tracking of objects in video sequences. In the proposed approach, a geometric template consisting of a small number of keypoints is used to track an object that moves smoothly. The coordinates of the keypoints and their neighborhood relations are associated with the coordinates of the nodes of a...

In this thesis, a study on gene expression data analysis is done using some supervised, unsupervised and semi-supervised approaches. The task of class prediction for six gene expression datasets (namely, Brain Tumor, Colon Cancer, Leukemia, Lymphoma and SRBCT) has been carried out. Here, a one-dimensional self-organizing feature maps (SOFM) in a semi-supervised learning framework is developed f...

Automatic fuzzy membership generation is important in pattern recognition. A new scheme is proposed to generate fuzzy membership functions with unsupervised learning using self-organizing feature map. Simulation results on different datasets support this new scheme. 2005 Elsevier B.V. All rights reserved. PACS: 07.05.Mh