We study the problem of visualizing large networks and develop es for effectively abstracting a network and reducing the size to a level that can be clearly viewed. Our size reduc...
A wide variety of machine learning problems can be described as minimizing a regularized risk functional, with different algorithms using different notions of risk and different r...
Choon Hui Teo, Alex J. Smola, S. V. N. Vishwanatha...
Several important time series data mining problems reduce to the core task of finding approximately repeated subsequences in a longer time series. In an earlier work, we formalize...
Bill Yuan-chi Chiu, Eamonn J. Keogh, Stefano Lonar...
We introduce a new method to derive lower bounds on randomized and quantum communication complexity. Our method is based on factorization norms, a notion from Banach Space theory....
Existing work on similar sequence matching has focused on either whole matching or range subsequence matching. In this paper, we present novel methods for ranked subsequence match...