In this paper, we develop a geometric framework for linear or nonlinear discriminant subspace learning and classification. In our framework, the structures of classes are conceptu...
We develop a neural network that learns to separate the nominal from the faulty instances of a circuit in a measurement space. We demonstrate that the required separation boundari...
- Contiguity Analysis is a straightforward generalization of Linear Discriminant Analysis in which the partition of elements is replaced by a more general graph structure. Applied ...
We present a general framework for analysis and design of optimization based numerical feedback stabilization schemes utilizing ideas from relaxed dynamic programming. The applicat...
This paper deals with the problem of structuralizing education and training videos for high-level semantics extraction and nonlinear media presentation in e-learning applications....