We introduce flexible algorithms that can automatically learn mappings from images to actions by interacting with their environment. They work by introducing an image classifier i...
Many real-world classification tasks involve the prediction of multiple, inter-dependent class labels. A prototypical case of this sort deals with prediction of a sequence of labe...
We investigate how stack filter function classes like weighted order statistics can be applied to classification problems. This leads to a new design criteria for linear classifie...
Reid B. Porter, Damian Eads, Don R. Hush, James Th...
We propose a new statistical approach to extracting personal names from a corpus. One of the key points of our approach is that it can both automatically learn the characteristics...
We consider the problem of unsupervised classification of temporal sequences of facial expressions in video. This problem arises in the design of an adaptive visual agent, which m...