Name
Abstract | ||
|---|---|---|
This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief. These relations, possibly regionalized and non- linear, can be modeled from data with machine learning using the digital elevation models in semi-supervised kernel methods. The range of the tools and methodological issues discussed in the study includes feature selection and semi- supervised Support Vector algorithms. The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach. |
Year | Venue | Keywords |
|---|---|---|
2008 | ESANN | kernel method,data model,spatial data,support vector,digital elevation model,satisfiability,feature selection,machine learning |
Field | DocType | Citations |
Spatial analysis,Data mining,Feature selection,Computer science,Polynomial kernel,Artificial intelligence,Environmental data,Manifold,Pattern recognition,Kernel embedding of distributions,Support vector machine,Kernel method,Machine learning | Conference | 1 |
PageRank | References | Authors |
0.36 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexei Pozdnoukhov | 1 | 216 | 18.87 |
| Mikhail F. Kanevski | 2 | 154 | 19.67 |