A graphical self-organizing approach to classifying electronic meeting output
(1997) A graphical self-organizing approach to classifying electronic meeting output. Journal of the American Society for Information Science 48(2):pp. 157-170.
Full text available as: |
Abstract
This article describes research in the application of a Kohonen Self-Organizing Map (SOM) to the problem of classification of electronic brainstorming output and an evaluation of the results. This research builds upon previous work in automating the meeting classification process using a Hopfield neural network. Evaluation of the Kohonen output comparing it with Hopfield and human expert output using the same set of data found that the Kohonen SOM performed as well as a human expert in representing term association in the meeting output and outperformed the Hopfield neural network algorithm. Recall of consensus meeting concepts and topics using the Kohonen algorithm was equivalent to that of the human expert.
| EPrint Type: | Journal Article (Paginated) |
|---|---|
| Keywords: | National Science Digital Library, NSDL, Artificial Intelligence Lab, AI Lab |
| Subjects: | Artificial Intelligence Knowledge Management |
| ID Code: | 527 |
| Deposited On: | 29 October 2004 |
| Alternative Locations: | http://ai.bpa.arizona.edu/go/papers.html |
| Eprint Statistics: | View statistics for this eprint |
| Tell A Colleague: | Tell a colleague about it. |