Gladys Castillo

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Most of the work in machine learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem of learning when the class-probability distribution that generate the examples changes over time. We present a method for detection of changes in the probability distribution of examples.(More)
Most of the work in Machine Learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem of learning when the distribution that generates the examples changes over time. We present a method for detection of changes in the probability distribution of examples. The idea behind(More)
Machine Learning (ML) and Knowledge Discovery (KD) are research areas with several different applications but that share a common objective of acquiring more and new information from data. This paper presents an application of several ML techniques in the identification of the opponent team and also on the classification of robotic soccer formations in the(More)
0950-7051/$ see front matter 2013 Elsevier B.V. A http://dx.doi.org/10.1016/j.knosys.2013.03.006 ⇑ Corresponding author. Address: ETS Ingeniería In nos, 29071 Málaga, Spain. Tel.: +34 952132863; fax: E-mail address: jmcarmona@lcc.uma.es (J.M. Carm Email foldering is a challenging problem mainly due to its high dimensionality and dynamic nature. This work(More)
Most of supervised learning algorithms assume the stability of the target concept over time. Nevertheless in many real-user modeling systems, where the data is collected over an extended period of time, the learning task can be complicated by changes in the distribution underlying the data. This problem is known in machine learning as concept drift. The(More)