Discovering Multiple Constraints that are Frequently Approximately Satisfied
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Discovering Multiple Constraints that are Frequently Approximately Satisfied
Some high-dimensional datasets can be mod elled by assuming that there are many dif ferent linear constraints, each of which is Frequently Approximately Satisfied (FAS) by the data. The probability of a data vec tor under the model is then proportional to the product of the probabilities of its con straint violations. We describe three meth ods of learning products of constraints using a h...
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