The Self-Organizing Map (SOM) algorithm has attracted an ever increasing amount of interest among researches and practitioners in a wide variety of elds The SOM and a variant of it, the LVQ, have been analyzed extensively, a number of variants of them have been developed and, perhaps most notably, they have been applied extensively within elds ranging from engineering sciences to medicine, biology, and economics We have collected a comprehensive list of 3343 scienti c papers that use the algorithms, have bene ted from them, or contain analyses of them The list is intended to serve as a source for literature surveys We have provided both a thematic and a keyword index to help nding articles of interest

Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997

The present invention relates to a technique in the form of an exemplary computer vision system for detecting the orientation of text or features on an object of manufacture. In the present system, an image of the features or text (10) is used to extract lines using horizontal bitmap sums, and then individual symbols (12) using vertical bitmap sums while using thresholds with each of the sums. The separated symbols are then appropriately trimmed and scaled to provide individual normalized symbols (14). A Decision Module (15) comprising a Feed-Forward Neural network (21) and a sequential decision arrangement (23) determines the "up", "down" or "indeterminate" orientation of the text after a variable number of symbols have been processed. The system can then compare (24) the determined orientation with a database to further determine if the object is in the "right-side up" "upside-down" or "indeterminate" orientation.

A technique for object orientation detection using a feed-forward neural network

We present a modification of Kohonens SOFMin order to simulate cortical plasticity induced by coactivation patterns. This is accomplished by introducing a probabilistic mode of stimulus presentation and by substituting the winner-takes- all mechanism by selecting the winner from a set of best matching neurons.

Modification of Kohonen's SOFM to Simulate Cortical Plasticity Induced by Coactivation Input Patterns

ABC [Lin82]. ABCs [Col89]. ABP [Lip78]. Abstract [Bro69]. academic [Wal82]. acceptance [Gul62]. Access [Day83]. accessories [Lin30]. According [Ano86b]. accuracy [WT28]. achieve [MR92b]. ACM [Abr81]. acoustooptic [Ano96d]. acquisition [CBT88]. ACS [Dod97]. Adaptive [CW85]. Addressable [Ano86a]. Addressing [Ano86d]. Adjust [Ano85b]. Adobe [Ben95]. Advanced [Kim80]. Advances [CTC66]. Advantage [Zur85].

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A Bibliography of Publications about Typesetting

The relation of subsymbolic (neural computing) and symbolic computing has been a topic of intense discussion. We address some of the drawbacks of current expert system technology and study the possibility of using neural computing principles to improve their competence. In this paper we focus on the problem of using neural networks to implement expert system rule conditions. Our approach allows symbolic inference engines to make direct use of complex sensory input via so called detector predicates. We also discuss the use of self organizing Kohonen networks as a means to determine those attributes (properties) of data that reflect meaningful statistical relationships in the expert system input space. This mechanism can be used to address the defficult problem of conceptual clustering of information. The concepts introduced are illustrated by two application examples: an automatic inspection system for circuit packs and an expert system for respiratory and anesthesia monitoring. The adopted approach differs from the earlier research on the use of neural networks as expert systems, where the only method to obtain knowledge is learning from training data. In our approach the synergy of rules and detector predicates combines the advantages of both worlds: it maintains the clarity of the rule-based knowledge representation at the higher reasoning levels without sacrificing the power of noise-tolerant pattern association offered by neural computing methods.

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Implementing expert system rule conditions by neural networks

The problem of determining the orientation of printed text is considered. The problem differs considerably from traditional optical character recognition, and its application to automatic inspection requires efficient processing and highly accurate results. Two methods are described. The first is a feedforward network, with structure and parameters derived using optimal detection theory. The second method makes use of the learning vector quantization self-organizing networks of T. Kohonen (Self-organization and Associative Memory, Springer-Verlag, 1988). Experimental results and a complete implementation are described. Both techniques are found to be successful and their relative advantages are discussed. >

Tirri

Papers

A comparison of feedforward and self-organizing approaches to the font orientation problems