Name
Abstract | ||
|---|---|---|
We investigate prediction of users' desktop activities in the Unix domain. The learning techniques we explore do not require explicit user teaching. We show that simple efficient many-class learning can perform well for action prediction, significantly improving over previously published results and baselines. This finding is promising for various human-computer interaction scenarios where a rich set of potentially predictive features is available, where there can be many different actions to predict, and where there can be considerable nonstationarity. |
Year | Venue | Keywords |
|---|---|---|
2009 | IJCAI | explicit user teaching,desktop action,predictive feature,efficient online,rich set,unix domain,simple efficient many-class learning,desktop activity,various human-computer interaction scenario,considerable nonstationarity,different action,action prediction,human computer interaction |
Field | DocType | Citations |
Online learning,Computer science,Unix,Artificial intelligence,Machine learning | Conference | 6 |
PageRank | References | Authors |
0.42 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Omid Madani | 1 | 1109 | 88.93 |
| Hung Hai Bui | 2 | 1188 | 112.37 |
| Eric Yeh | 3 | 6 | 0.75 |