Internet agents who structure concept formation using kernel self-organising maps. (14th February 2005)

• Record Type: Journal Article
• Authors: Corchado, Emilio; MacDonald, Donald; Fyfe, Colin
• Is Part Of: International journal of Web engineering and technology. Volume 1:Number 4(2005)
• Page Start: 427
• Page End: 436
• Publication Date: 2005-02-14
• Subjects: self-organising maps -- kernel space -- internet agents -- feature space -- concept formation -- html documents -- human learning -- neural networks -- document classification -- text contents; World Wide Web -- Periodicals; Web site development -- Periodicals; Application software -- Development -- Periodicals; 006.7
• Languages: English
• ISSNs: 1476-1289

Abstract

We review a technique for creating self-organising maps (SOMs) in a feature space, which is nonlinearly related to the original data space. We show that convergence is remarkably fast for this method. We then consider the case where an internet agent quantises internet (HTML) documents using this method. The resulting map has two properties, which are interesting from a human perspective: first, the learning forms topology preserving mappings extremely quickly; second, the learning is most refined for those parts of the feature space which is learned first and which have most data. By considering the linear feature space, we show that it is the interaction between the overcomplete basis in which learning takes place and the mixture of one-shot and incremental learning which comprises the method that gives the method its power. Finally, as a tool to quantise document collections, the tool has the invaluable property that it scales with the number of documents on a site rather than the number of distinct words. We illustrate its use on a standard dataset.