: The k nearest neighbor classification (k-NN) is a very simple and popular method for classification. However, it suffers from a major drawback, it assumes constant local class po...
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Abstract. We present a hierarchical partitioning of images using a pairwise similarity function on a graph-based representation of an image. This function measures the difference ...
Online Analytical Processing (OLAP) data is frequently organized in the form of multidimensional data cubes each of which is used to examine a set of data values, called measures, ...
Traffic repositories with TCP/IP header information are very important for network analysis. Researchers often assume that such repositories reliably represent all traffic that has...