Support vector machine (SVM) classifiers are popular in many computer vision tasks. In most
of them, the SVM classifier assumes that the object to be classified is centered in the query …

Non-negative matrix factorization (NMF) has previously been shown to be a useful
decomposition tool for multivariate data. Non-negative bases allow strictly additive …

Leading classification methods such as support vector machines (SVMs) and their
counterparts achieve strong generalization performance by maximizing the margin of …

Linear filters are ubiquitously used as a preprocessing step for many classification tasks in
computer vision. In particular, applying Gabor filters followed by a classification stage, such …

We propose a novel stochastic Nesterov's smoothing accelerated method for general
nonsmooth, constrained, stochastic composite convex optimization, the nonsmooth …

What is called supervised learning is the most fundamental task in machine learning. In
supervised learning, we have training examples and test examples. A training example is an …

We propose a novel stochastic smoothing accelerated gradient (SSAG) method for general
constrained nonsmooth convex composite optimization, and analyze the convergence rates …

This paper describes a large, unabridged data-set of Java source code gathered and shared
as part of the Merobase Component Finder project of the Software-Engineering Group at the …

In this paper we propose a framework for gradient descent image alignment in the Fourier
domain. Specifically, we propose an extension to the classical Lucas & Kanade (LK) …

ABSTRACT A masquerade attack is a consequence of identity theft. In such attacks, the
impostor impersonates a legitimate insider while performing illegitimate activities. These …