Titlebook: Health Professions in Nigeria; An Interdisciplinary Joseph Abiodun Balogun Book 2021 The Editor(s) (if applicable) and The Author(s), under

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Confess

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 c

AWL

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

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 independent

Debark

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

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ANT

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

俗艷

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

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