Name
Abstract | ||
|---|---|---|
An algorithmic architecture for kernel-based modelling of data streams from city sensing infrastructures is introduced. It is both applicable for pre-installed, moving and extemporaneous sensors, including the “citizen-as-a-sensor” view on user-generated data. The approach is centred around a kernel dictionary implementing a general hypothesis space which is updated incrementally, accounting for memory and processing capacity limitations. It is general for both kernel-based classification and regression. An extension to area-to-point modelling is introduced to account for the data aggregated over a spatial region. A distributed implementation realised under the Map-Reduce framework is presented to train an ensemble of sequential kernel learners. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.pmcj.2012.11.003 | Pervasive and Mobile Computing |
Keywords | Field | DocType |
Sensor networks,Machine learning,Kernel methods,Spatial statistics,Smart cities | Kernel (linear algebra),Artificial architecture,Spatial analysis,Data mining,Data stream mining,Computer science,Tree kernel,Artificial intelligence,Kernel method,Wireless sensor network,Machine learning | Journal |
Volume | Issue | ISSN |
9 | 5 | 1574-1192 |
Citations | PageRank | References |
8 | 0.58 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Kaiser | 1 | 8 | 0.91 |
| Alexei Pozdnoukhov | 2 | 216 | 18.87 |