Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. There are...
The efficient solution of large-scale linear and nonlinear optimization problems may require exploiting any special structure in them in an efficient manner. We describe and analy...
Antonio J. Conejo, Francisco J. Nogales, Francisco...
In the recent years support vector machines (SVMs) have been successfully applied to solve a large number of classification problems. Training an SVM, usually posed as a quadrati...
Several authors have suggested viewing boosting as a gradient descent search for a good fit in function space. At each iteration observations are re-weighted using the gradient of...
Boosting is a popular approach for building accurate classifiers. Despite the initial popular belief, boosting algorithms do exhibit overfitting and are sensitive to label noise. ...