version space
online_pmc-2
However, the completion accuracy can be drastically different over different entries.
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TowardsaUnified Information-Theoretic FrameworkforGeneralization
Let D be an unknown distribution on a spaceZ, and let H be a set of classifiers. Consider a (randomized) learning algorithmA = (An)n 1 that selects an elementˆh in H, based onn i.i.d.
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- Overview (0.67)
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- Europe > United Kingdom > England > Oxfordshire > Oxford (0.04)
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6a26c75d6a576c94654bfc4dda548c72-Paper.pdf
Forlinear regression, we give a polynomial-time algorithm based on Celis-Dennis-Tapia optimization algorithms. For binary classification, we show how to efficiently implement itusing aproper agnostic learner (i.e., anEmpirical Risk Minimizer) for the class of interest.
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- North America > United States > Massachusetts > Middlesex County > Cambridge (0.14)
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- Asia > Middle East > Jordan (0.04)
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Quantum Perceptron Models
Ashish Kapoor, Nathan Wiebe, Krysta Svore
We develop two quantum algorithms for perceptron learning.
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