Online learning and kernel learning are two active research topics in machine learning. Although each of them has been studied extensively, there is a limited effort in addressing ...
Bounds are given for the empirical and expected Rademacher complexity of classes of linear transformations from a Hilbert space H to a ...nite dimensional space. The results imply ...
We address the problem of online de-noising a stream of input points. We assume that the clean data is embedded in a linear subspace. We present two online algorithms for tracking ...
We propose online decision strategies for time-dependent sequences of linear programs which use no distributional and minimal geometric assumptions about the data. These strategies...
Tatsiana Levina, Yuri Levin, Jeff McGill, Mikhail ...
We present a discrete spectral framework for the sparse or cardinality-constrained solution of a generalized Rayleigh quotient. This NPhard combinatorial optimization problem is c...