Rafael Morales Bueno

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In the process of concept learning, target concepts may have portions with short-term changes, other portions may support long-term changes, and yet others may not change at all. For this reason several local windows need to be handled. We suggest facing this problem, which naturally exists in the field of concept learning, by allocating windows which can(More)
Real-time classification of emails is a challenging task because of its online nature, and also because email streams are subject to concept drift. Identifying email spam, where only two different labels or classes are defined (spam or not spam), has received great attention in the literature. We are nevertheless interested in a more specific classification(More)
In order to discover behavior patterns, current algorithms only analyze historical data in terms of performance data or fault events, ignoring the temporal correlation among different types of information, including the configuration changes. A method is presented that can discover recurrent patterns from multiple flows of events, such as alarms and(More)
In this paper we first compare Parikh’s condition to various pumping conditions ~ BarHillel’s pumping lemma, Ogden’s condition and Bader-Moura’s condition; secondly, to interchange condition; and finally, to Sokolowski’s and Grant“s conditions. In order to carry out these comparisons we present some properties of Parikh’s languages. The main result is the(More)
Real-time classification of massive email data is a challenging task that presents its own particular difficulties. Since email data presents an important temporal component, several problems arise: emails arrive continuously, and the criteria used to classify those emails can change, so the learning algorithms have to be able to deal with concept drift.(More)
A model to characterize and predict continuous time series from machine-learning techniques is proposed. This model includes the following three steps: dynamic discretization of continuous values, construction of probabilistic finite automata and prediction of new series with randomness. The first problem in most models from machine learning is that they(More)