In microarray classification we are faced with a very large number of features and very few training samples. This is a challenge for classical Linear Discriminant Analysis (LDA),...
Roger Pique-Regi, Antonio Ortega, Shahab Asgharzad...
The basic idea of an algebraic approach to learning Bayesian network (BN) structures is to represent every BN structure by a certain uniquely determined vector, called the standar...
K-Nearest Neighbors relies on the definition of a global metric. In contrast, Discriminant Adaptive Nearest Neighbor (DANN) computes a different metric at each query point based o...
As more complex DSP algorithms are realized in practice, an increasing need for high-level stream abstractions that can be compiled without sacrificing efficiency. Toward this en...
Andrew A. Lamb, William Thies, Saman P. Amarasingh...
This paper describes a mode detection system for online pen input that employs a Bayesian network to combine classification results and context information. Previous monolithic c...