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Structural Information Learning Machinery: Learning from Observing, Associating, Optimizing, Decoding, and Abstracting
[article]
2020
arXiv
pre-print
In the present paper, we propose the model of structural information learning machines (SiLeM for short), leading to a mathematical definition of learning by merging the theories of computation and information. Our model shows that the essence of learning is to gain information, that to gain information is to eliminate uncertainty embedded in a data space, and that to eliminate uncertainty of a data space can be reduced to an optimization problem, that is, an information optimization problem,
arXiv:2001.09637v1
fatcat:nkt6efij3zcr3g7oid36gvktge