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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Peterson, A. Martinez, T. Rudolph, G. |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Mathematics and Computer Science, The Citadel, Charleston, SC, 29409, USA (Rudolph, G.) || Department of Computer Science, Brigham Young University, Provo, UT, 84602, USA (Martinez, T.) || Adobe Systems, Orem UT, 84097, USA (Peterson, A.) |
| Abstract | Many learning algorithms have been developed to solve various problems. Machine learning practitioners must use their knowledge of the merits of the algorithms they know to decide which to use for each task. This process often raises questions such as: (1) If performance is poor after trying certain algorithms, which should be tried next? (2) Are some learning algorithms the same in terms of actual task classification? (3) Which algorithms are most different from each other? (4) How different? (5) Which algorithms should be tried for a particular problem? This research uses the COD (Classifier Output Difference) distance metric for measuring how similar or different learning algorithms are. The COD quantifies the difference in output behavior between pairs of learning algorithms. We construct a distance matrix from the individual COD values, and use the matrix to show the spectrum of differences among families of learning algorithms. Results show that individual algorithms tend to cluster along family and functional lines. Our focus, however, is on the structure of relationships among algorithm families in the space of algorithms, rather than on individual algorithms. A number of visualizations illustrate these results. The uniform numerical representation of COD data lends itself to human visualization techniques. |
| Starting Page | 658 |
| Ending Page | 665 |
| File Size | 397347 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424496358 |
| ISSN | 21614407 |
| e-ISBN | 9781424496372 |
| DOI | 10.1109/IJCNN.2011.6033284 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-07-31 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Machine learning algorithms Measurement Clustering algorithms Data visualization Machine learning Decision trees Accuracy |
| Content Type | Text |
| Resource Type | Article |
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