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Structure space of Bayesian networks is dramatically reduced by subdividing it in sub-networks. (English) Zbl 1329.68207
Summary: Currently, Bayesian Networks (BNs) have become one of the most complete, self-sustained and coherent formalisms used for knowledge acquisition, representation and application through computer systems. However, learning of BNs structures from data has been shown to be an NP-hard problem. It has turned out to be one of the most exciting challenges in machine learning. In this context, the present work’s major objective lies in setting up a further solution conceived to be a remedy for the intricate algorithmic complexity imposed during the learning of BN-structure with a massively-huge data backlog.
MSC:
68T05 Learning and adaptive systems in artificial intelligence
68Q25 Analysis of algorithms and problem complexity
Software:
BNT; ClustOfVar; TETRAD
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