Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

Concept drift primarily refers to an online supervised learning scenario when the relation
between the input data and the target variable changes over time. Assuming a general …

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Major assumptions in computational intelligence and machine learning consist of the
availability of a historical dataset for model development, and that the resulting model will, to …

In this paper we present a broad overview of the last 40 years of research on cognitive
architectures. To date, the number of existing architectures has reached several hundred …

In supervised machine learning, an agent is typically trained once and then deployed. While
this works well for static settings, robots often operate in changing environments and must …

In malicious URLs detection, traditional classifiers are challenged because the data volume
is huge, patterns are changing over time, and the correlations among features are …

In hyperspectral unmixing, the prevalent model used is the linear mixing model, and a large
variety of techniques based on this model has been proposed to obtain endmembers and …

When an agent acquires new information, ideally it would immediately be capable of using
that information to understand its environment. This is not possible using conventional deep …

Machine learning has proven to be a powerful technique during the past decades. Artificial
neural network (ANN), as one of the most popular machine learning algorithms, has been …