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ng from Chap. .) have we extended it to substantially different statistical models. Interestingly, conformal prediction in this wider sense is much more familiar to statisticians. In this concluding chapter, we step back to survey the historical context of conformal prediction, contrasting it with cAWL 发表于 2025-3-22 06:15:24
Joseph Abiodun Balogunal prediction is the principled treatment of the reliability of predictions. The prediction algorithms described — conformal predictors — are provably valid in the sense that they evaluate the reliability of their own predictions in a way that is neither over-pessimistic nor over-optimistic (the lat灰姑娘 发表于 2025-3-22 09:43:37
Joseph Abiodun Balogunv‘s algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independentDebark 发表于 2025-3-22 13:02:25
Joseph Abiodun Balogunachine learning, and unlike other state-of-the-art methods, .Algorithmic Learning in a Random World. describes recent theoretical and experimental developments in building computable approximations to Kolmogorov‘s algorithmic notion of randomness. Based on these approximations, a new set of machine节约 发表于 2025-3-22 19:50:04
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Joseph Abiodun Balogunachine learning, and unlike other state-of-the-art methods, .Algorithmic Learning in a Random World. describes recent theoretical and experimental developments in building computable approximations to Kolmogorov‘s algorithmic notion of randomness. Based on these approximations, a new set of machine俗艳 发表于 2025-3-23 05:01:25
v‘s algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent抗生素 发表于 2025-3-23 07:52:09
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