- Related Research Areas
- Data Mining and Knowledge Discovery
We conducted a study on developing an anomaly detector that can run on heterogeneous data sets. This research resulted in a newly developed version of classical One Class SVMs called MKAD (Multiple Kernel Anomaly Detection) algorithm which can efficiently handle heterogeneous data and conduct fleet wide analysis. We have demonstrated the automated anomaly detection in an off-line mode on large heterogeneous data sets from multiple aircraft. We also demonstrate ability to perform anomaly detection on a data set containing both discrete symbols and continuous data streams and show a 100% detection rate.
A Publication, Multiple Kernel Learning based Heterogeneous Algorithm (MKAD) - 1 year, 3 months ago
Shared By: Nikunj Oza
The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern ...
A Publication, Multiple Kernel Learning based Heterogeneous Algorithm (MKAD) - 2 years, 7 months ago
Shared By: Santanu Das
Published at 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSM 17 - 19 September 2012, Indianapolis, Indiana
An Algorithm, Multiple Kernel Learning based Heterogeneous Algorithm (MKAD) - 3 years, 4 months ago
Shared By: Indir Jaganjac
Learning and building GMDH polynomial neural networks and printing them as 3rd degree polynomial equation.
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