Name
Abstract | ||
|---|---|---|
Complex simulations can generate very large amounts of data stored disjointly across many local disks. Learning from this data can be problematic due to the difficulty of obtaining labels for the data. We present an algorithm for the application of semi-supervised learning on disjoint data generated by complex simulations. Our semi-supervised technique shows a statistically significant accuracy improvement over supervised learning using the same underlying learning algorithm and requires less labeled data for comparable results. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICPR.2008.4761797 | 19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6 |
Keywords | Field | DocType |
accuracy,classification algorithms,learning artificial intelligence,data models,semi supervised learning,computational modeling,supervised learning,statistical significance,force | Online machine learning,Data modeling,Stability (learning theory),Instance-based learning,Semi-supervised learning,Pattern recognition,Computer science,Supervised learning,Unsupervised learning,Artificial intelligence,Statistical classification,Machine learning | Conference |
ISSN | Citations | PageRank |
1051-4651 | 3 | 0.39 |
References | Authors | |
8 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| John Nicholas Korecki | 1 | 3 | 0.39 |
| Robert E. Banfield | 2 | 358 | 17.16 |
| Larry O. Hall | 3 | 5 | 0.78 |
| Kevin W. Bowyer | 4 | 11121 | 734.33 |
| W. Philip Kegelmeyer | 5 | 3498 | 146.54 |