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Abstract
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| Probabilistic graphical models (PGMs) are a competitive tool that allows discovering useful knowledge from data, and its posterior exploitation (by means of inference). Although the field of PGMs exhibits nowadays a high degree of maturity, more research is necessary to extend its applicability as a data mining tool to more complex problems in existing real-world applications, or solve new ones. In this project we propose a joint effort to advance to this research line, by means of a coordinated project formed by four groups that have previously demonstrated their research experience by making substantial contributions to the state-of-the-art on PGMs, and with a high degree of interconnection acquired by previous coordinated projects and collaborations. The main objective of this project is to advance in different topics related to PGMs, so that we will obtain better results than previous approaches, both because we enlarge the class of in-practice solvable problems, or because we face new/recent challenges. The core package of the algorithms to be developed has a direct application in several stages of the Knowledge Discovering from Databases (KDD) cycle, as preprocessing, (supervised and unsupervised) data mining and knowledge exploitation (inference). Also, during the development of these new algorithms we must bear in mind a set of challenging real applications we have selected to be included (and that serve as testbeds) in this project. | |
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International
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No |
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Project type
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Proyectos y convenios en convocatorias públicas competitivas |
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Company
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Ministerio de Innovación y Ciencia |
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Entity Nationality
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ESPAÑA |
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Entity size
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Pequeña Empresa (11-50) |
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Granting date
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