It is frequently remarked that designers of computer vision algorithms and systems cannot reliably predict how algorithms will respond to new problems. A variety of reasons have b...
Neil A. Thacker, Adrian F. Clark, John L. Barron, ...
We present a statistical model of empirical optimization that admits the creation of algorithms with explicit and intuitively defined desiderata. Because No Free Lunch theorems di...
We use large deviations to prove a general theorem on the asymptotic edge-weighted height Hn of a large class of random trees for which Hn c log n for some positive constant c. A...
In this work, we introduce a new framework able to deal with a reasoning that is at the same time non monotonic and uncertain. In order to take into account a certainty level assoc...
A procedure is described for finding sets of key players in a social network. A key assumption is that the optimal selection of key players depends on what they are needed for. Acc...