Learning using the Born Rule
dc.date.accessioned | 2006-05-16T22:16:59Z | |
dc.date.accessioned | 2018-11-24T10:24:52Z | |
dc.date.available | 2006-05-16T22:16:59Z | |
dc.date.available | 2018-11-24T10:24:52Z | |
dc.date.issued | 2006-05-16 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/32978 | |
dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/1721.1/32978 | |
dc.description.abstract | In Quantum Mechanics the transition from a deterministic descriptionto a probabilistic one is done using a simple rule termed the Bornrule. This rule states that the probability of an outcome ($a$)given a state ($\Psi$) is the square of their inner products($(a^\top\Psi)^2$).In this paper, we unravel a new probabilistic justification forpopular algebraic algorithms, based on the Born rule. Thesealgorithms include two-class and multiple-class spectral clustering,and algorithms based on Euclidean distances. | |
dc.format.extent | 28 p. | |
dc.format.extent | 650559 bytes | |
dc.format.extent | 1572192 bytes | |
dc.language.iso | en_US | |
dc.title | Learning using the Born Rule |
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