We consider the problem of clustering in its most basic form where only a local metric on the data space is given. No parametric statistical model is assumed, and the number of cl...
We introduce a novel framework for simultaneous structure and parameter learning in hidden-variable conditional probability models, based on an entropic prior and a solution for i...
In an uncertain data set S = (S, p, f) where S is the ground set consisting of n elements, p : S → [0, 1] a probability function, and f : S → R a score function, each element i...
Background: The mechanisms underlying protein function and associated conformational change are dominated by a series of local entropy fluctuations affecting the global structure ...
Most recent research on querying and managing data streams has concentrated on traditional data models where the data come in the form of tuples or XML data. Complex types of strea...