Matching Pursuit algorithms learn a function that is a weighted sum of basis functions, by
sequentially appending functions to an initially empty basis, to approximate a target function …

We investigate data based procedures for selecting the kernel when learning with Support
Vector Machines. We provide generalization error bounds by estimating the Rademacher …

We explore an algorithm for training SVMs with Kernels that can represent the learned rule
using arbitrary basis vectors, not just the support vectors (SVs) from the training set. This …

While classical kernel-based learning algorithms are based on a single kernel, in practice it
is often desirable to use multiple kernels. Lanckriet et al.(2004) considered conic …

In numerous domains it is useful to represent a single example by the set of the local
features or parts that comprise it. However, this representation poses a challenge to many …

Given a large number of basis functions that can be potentially more than the number of
samples, we consider the problem of learning a sparse target function that can be expressed …

We investigate implicit regularization schemes for gradient descent methods applied to
unpenalized least squares regression to solve the problem of reconstructing a sparse signal …

In this thesis we consider statistical learning problems and machines. A statistical learning
machine tries to infer rules from a given set of examples such that it is able to make correct …

In this paper, we consider the problem of compressed sensing where the goal is to recover
almost all the sparse vectors using a small number of fixed linear measurements. For this …

This paper presents new and effective algorithms for learning kernels. In particular, as
shown by our empirical results, these algorithms consistently outperform the so-called …