Name
Abstract | ||
|---|---|---|
Kernel methods became one of the mainstreams of machine learning research in recent decades. This success is due to their ability to provide robust non-linear models with good generalization abilities which are easy to train and interpret. Spatial heterogeneity, prior knowledge on spatial similarities, discontinuities, physical and administrative boundaries can be incorporated into kernel modeling frameworks but require special consideration. This paper describes a general framework for building spatial extensions to kernel methods via data-driven kernel transforms. It is illustrated numerically by constructing spatial extensions to kernels for environmental and remote sensing applications. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1869790.1869871 | GIS |
Keywords | Field | DocType |
kernel modeling framework,spatial heterogeneity,general framework,administrative boundary,kernel method,spatial extension,prior knowledge,data-driven kernel,spatial similarity,good generalization ability,remote sensing,machine learning,kernel methods,spatial statistics | Spatial analysis,Graph kernel,Radial basis function kernel,Kernel embedding of distributions,Computer science,Geometric modeling kernel,Tree kernel,Polynomial kernel,Artificial intelligence,Kernel method,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.37 | 2 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexei Pozdnoukhov | 1 | 216 | 18.87 |