On-line probabilistic classification with particle filters
Pedro A.d.F.R. Højen-Sørensen, Nando de Freitas, T.L. Fog · 2002
We apply particle filters to the problem of on-line classification with possibly overlapping classes. This allows us to compute the probabilities of class membership as the classes evolve. Although we adopt neural network classifiers, the work can be extended to any other parametric classification scheme. We demonstrate our methodology on a simple example and on the problem of fault detection of dynamically operated marine diesel engines.