act 11 We describe an ensemble approach to learning from arbitrarily partitioned data. The partitioning comes from the distributed process12 ing requirements of a large scale simul...
Larry Shoemaker, Robert E. Banfield, Lawrence O. H...
We describe a framework for learning an object classifier from a single example. This goal is achieved by emphasizing the relevant dimensions for classification using available ex...
Most proteins function only when folded into a particular 3D configuration. Recently, a class of proteins has been discovered that do not fold into any particular configuration; t...
3D surface classification is a fundamental problem in computer vision and computational geometry. Surfaces can be classified by different transformation groups. Traditional classi...
Probability theory is the framework for making decision under uncertainty. In classification, Bayes' rule is used to calculate the probabilities of the classes and it is a bi...
Mohammed J. Islam, Q. M. Jonathan Wu, Majid Ahmadi...