Gregory J. Wolff

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Our contextual inquiry into the practices of oral historians unearthed a curious incongruity. While oral historians consider interview recordings a central historical artifact, these recordings sit unused after a written transcript is produced. We hypothesized that this is largely because books are more usable than recordings. Therefore, we created Books(More)
We extend Optimal Brain Surgeon (OBS)-a second-order method for pruning networks-to allow for general error measures , and explore a reduced computational and storage implementation via a dominant eigenspace decomposition. Simulations on nonlinear, noisy pattern classification problems reveal that OBS does lead to improved generalization, and performs(More)
Our contextual inquiry into the practices of oral historians unearthed a curious incongruity: while oral historians consider interview recordings to be a central historical artifact, these recordings sit unused after a written transcript is produced. We hypothesized that this is largely because books are more usable than recordings, so we created Books with(More)
We have developed visual preprocessing algorithms for extracting phonologically relevant features from the grayscale video image of a speaker, to provide speaker-independent inputs for an automatic lipreading ("speechreading") system. Visual features such as mouth open/closed, tongue visible/not-visible, teeth visible/not-visible, and several shape(More)
This paper proposes a novel user interface to manage the dynamic layout of multimedia objects in the Multimedia Bulletin Board (MBB) system. The MBB has been designed and implemented as a prototype of an asynchronous communication system that enables rich communication and collaboration among users of multimedia objects such as text, image, moving picture,(More)
"Error-Confidence" measures the probability that the proportion of errors made by a classifier will be within epsilon of EB, the optimal (Bayes) error. Probably Almost Bayes (PAB) theory attempts to quantify how this confidence increases with the number of training samples. We investigate the relationship empirically by comparing average error versus number(More)