In most challenging applications learning algorithms acts in dynamic environments where
the data is collected over time. A desirable property of these algorithms is the ability of …

In most challenging applications learning algorithms act in dynamic environments where the
data is collected over time. A desirable property of these algorithms is the ability of …

A fundamental problem when performing incremental learning is that the best set of a
classification system's parameters can change with the evolution of the data. Consequently …

In the real word, the environment is often dynamic instead of stable. Usually the underlying
data of a problem changes with time, which enhances the difficulties when learning a model …

Methods involving ensembles of classifiers, such as bagging and boosting, are popular due
to the strong theoretical guarantees for their performance and their superior results …

Classification is a quite relevant task within data analysis field. This task is not a trivial task
and different difficulties can arise depending on the nature of the problem. All these …

We describe an incremental learning algorithm designed to learn in challenging non-
stationary environments, where the underlying data distribution that governs the …

The incremental updating of classifiers implies that their internal parameter values can vary
according to incoming data. As a result, in order to achieve high performance, incremental …

Incremental learning is a machine learning paradigm where the learning process takes
place whenever new example/s emerge and adjusts what has been learned according to …

We describe an ensemble of classifiers based approach for incrementally learning from new
data drawn from a distribution that changes in time, ie, data obtained from a nonstationary …