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FLAIRS
2004

Blind Data Classification Using Hyper-Dimensional Convex Polytopes

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Blind Data Classification Using Hyper-Dimensional Convex Polytopes
A blind classification algorithm is presented that uses hyperdimensional geometric algorithms to locate a hypothesis, in the form of a convex polytope or hyper-sphere. The convex polytope geometric model provides a well-fitted class representation that does not require training with instances of opposing classes. Further, the classification algorithm creates models for as many training classes of data as are available resulting in a hybrid anomaly/signature-based classifier. A method for handling non-numeric data types is explained. Classification accuracy is enhanced through the
Brent T. McBride, Gilbert L. Peterson
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where FLAIRS
Authors Brent T. McBride, Gilbert L. Peterson
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