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dc.contributor.advisorTomaso Poggio
dc.contributor.authorWolf, Lior
dc.contributor.otherVision
dc.date.accessioned2006-05-16T22:16:59Z
dc.date.available2006-05-16T22:16:59Z
dc.date.issued2006-05-16
dc.identifier.otherMIT-CSAIL-TR-2006-036
dc.identifier.otherCBCL-261
dc.identifier.urihttp://hdl.handle.net/1721.1/32978
dc.description.abstractIn 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.extent28 p.
dc.format.extent650559 bytes
dc.format.extent1572192 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.language.isoen_US
dc.relation.ispartofseriesMassachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
dc.titleLearning using the Born Rule


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