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In this article we present the so-called continuous classifying associative memory, able to store continuous patterns avoiding the problems of spurious states and data dependency. This is a memory model based on our previously developed classifying associative memory, which enables continuous patterns to be stored and recovered. We will also show that the(More)
present a new associative memory model that stores arbitrary bipolar patterns without the problems we can find in other models like BAM or LAM. After identifying those problems we show the new memory topology and we explain its learning and recall stages. Mathematical demonstrations are provided to prove that the new memory model guarantees the perfect(More)
The incorporation of temporal semantics into the traditional data mining techniques has caused the creation of a new area called Temporal Data Mining. This incorporation is especially necessary if we want to extract useful knowledge from dynamic domains, which are time-varying in nature. Related to this topic, we proposed in [11] an algorithm named T SET(More)