We investigate the problem of estimating the proportion vector which maximizes the likelihood of a given sample for a mixture of given densities. We adapt a framework developed for...
David P. Helmbold, Yoram Singer, Robert E. Schapir...
In this paper we propose a Gaussian-kernel-based online kernel density estimation which can be used for applications of online probability density estimation and online learning. ...
To analyze time-varying data sets, tracking features over time is often necessary to better understand the dynamic nature of the underlying physical process. Tracking 3D time-vary...
Let G = (V, E) be an weighted undirected graph on n vertices and m edges, and let dG be its shortest path metric. We present two simple deterministic algorithms for approximating ...
Background: Phylogenetic methods which do not rely on multiple sequence alignments are important tools in inferring trees directly from completely sequenced genomes. Here, we exte...
Alexander F. Auch, Stefan R. Henz, Barbara R. Holl...