Ensemble methods use multiple models to get better performance. Ensemble methods have
been used in multiple research fields such as computational intelligence, statistics and …

In this monograph, I introduce the basic concepts of Online Learning through a modern view
of Online Convex Optimization. Here, online learning refers to the framework of regret …

Anomalous event detection is of utmost importance in intelligent video surveillance.
Currently, most approaches for the automatic analysis of complex video scenes typically rely …

Multiple kernel learning (MKL) has been proposed for kernel methods by learning the
optimal kernel from a set of predefined base kernels. However, the traditional L 1 MKL …

Kernel adaptive filters (KAF) are a class of powerful nonlinear filters developed in
Reproducing Kernel Hilbert Space (RKHS). The Gaussian kernel is usually the default …

In this paper a novel and effective approach for automated audio classification is presented
that is based on the fusion of different sets of features, both visual and acoustic. A number of …

Building highly nonlinear and nonparametric models is central to several state-of-the-art
machine learning systems. Kernel methods form an important class of techniques that …

Crowdsourcing has been proven to be an effective and efficient tool to annotate large data-
sets. User annotations are often noisy, so methods to combine the annotations to produce …

In supervised dimensionality reduction methods for pattern recognition tasks, the information
of the class labels is considered through the process of reducing the input dimensionality, to …

Data-driven methods have been successfully used in modern industrial production. The
sufficient data is the basis for implementing these methods. However, it is often impossible to …