Self-organizing maps
Operating systems (3rd ed.): internals and design principles
Operating systems (3rd ed.): internals and design principles
Distributed systems (3rd ed.): concepts and design
Distributed systems (3rd ed.): concepts and design
Self organization of a massive document collection
IEEE Transactions on Neural Networks
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This paper presents how Self-Organizing Maps (SOMs)can be trained efficiently using several, simultaneously executing threads on a shared memory Symmetric MultiProcessing (SMP)computer. The training method is a batch version of the Tree-Structured Self-Organizing Map. We note that SMP type of parallel training is very useful for large data sets obtained from nature, the process industry or large document collections, since we do not encounter similar model size limitations as with hardware SOM implementations.