A graphical self-organizing approach to classifying electronic meeting output
| dc.contributor.author | Orwig, Richard E. | |
| dc.contributor.author | Chen, Hsinchun | |
| dc.contributor.author | Nunamaker, Jay F. | |
| dc.date.accessioned | 2004-10-29T00:00:01Z | |
| dc.date.available | 2010-06-18T23:31:47Z | |
| dc.date.issued | 1997-02 | en_US |
| dc.date.submitted | 2004-10-29 | en_US |
| dc.identifier.citation | A graphical self-organizing approach to classifying electronic meeting output 1997-02, 48(2):157-170 Journal of the American Society for Information Science | en_US |
| dc.identifier.uri | http://hdl.handle.net/10150/105681 | |
| dc.description | Artificial Intelligence Lab, Department of MIS, University of Arizona | en_US |
| dc.description.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. | |
| dc.format.mimetype | application/pdf | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Wiley Periodicals, Inc | en_US |
| dc.subject | Artificial Intelligence | en_US |
| dc.subject | Knowledge Management | en_US |
| dc.subject.other | National Science Digital Library | en_US |
| dc.subject.other | NSDL | en_US |
| dc.subject.other | Artificial intelligence lab | en_US |
| dc.subject.other | AI lab | en_US |
| dc.title | A graphical self-organizing approach to classifying electronic meeting output | en_US |
| dc.type | Journal Article (Paginated) | en_US |
| dc.identifier.journal | Journal of the American Society for Information Science | en_US |
| refterms.dateFOA | 2018-08-21T13:32:08Z | |
| html.description.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. |
