Results 11 to 20 of about 2,032,077 (205)
Semantic Probabilistic Inference of Predictions
Известия Иркутского государственного университета: Серия "Математика", 2017 Prediction is one of the most important concepts in science. Predictions obtained from probabilistic knowledge, are described by an inductive-statistical inference (I-S inference).
E. E. Vityaev
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Metrics of calibration for probabilistic predictions
CoRR, 2022 50 pages, 36 ...
Imanol Arrieta Ibarra +4 more
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Probabilistic Predictions with Federated Learning [PDF]
Entropy, 2020 Probabilistic predictions with machine learning are important in many applications. These are commonly done with Bayesian learning algorithms. However, Bayesian learning methods are computationally expensive in comparison with non-Bayesian methods. Furthermore, the data used to train these algorithms are often distributed over a large group of end ...
Adam Thor Thorgeirsson, Frank Gauterin
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Learning probabilistic prediction functions [PDF]
[Proceedings 1988] 29th Annual Symposium on Foundations of Computer Science, 1988 The question of how to learn rules, when those rules make probabilistic statements about the future, is considered. Issues are discussed that arise when attempting to determine what a good prediction function is, when those prediction functions make probabilistic assumptions. Learning has at least two purposes: to enable the learner to make predictions
A. Desantis, G. Markowski, M. Wegman
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Probabilistically robust conformal prediction
CoRR, 2023 Conformal prediction (CP) is a framework to quantify uncertainty of machine learning classifiers including deep neural networks. Given a testing example and a trained classifier, CP produces a prediction set of candidate labels with a user-specified coverage (i.e., true class label is contained with high probability). Almost all the existing work on CP
Subhankar Ghosh +5 more
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Predictive Multiplicity in Probabilistic Classification
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Machine learning models are often used to inform real world risk assessment tasks: predicting consumer default risk, predicting whether a person suffers from a serious illness, or predicting a person's risk to appear in court. Given multiple models that perform almost equally well for a prediction task, to what extent do predictions vary across these ...
Jamelle Watson-Daniels +2 more
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Energies, 2021 In this study, we propose a novel workflow to predict the production of existing and new multi-wells. To perform reliable production forecasting on heterogeneous shale formations, the features of these formations must be analyzed by classifying the ...
Hyo-Jin Shin, Jong-Se Lim, Il-Sik Jang
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From Conformal to Probabilistic Prediction [PDF]
, 2014 This paper proposes a new method of probabilistic prediction, which is based on conformal prediction. The method is applied to the standard USPS data set and gives encouraging results.
Vladimir Vovk +2 more
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Probabilistic Prediction of Temporal Locality [PDF]
IEEE Computer Architecture Letters, 2007 The increasing gap between processor and memory speeds, as well as the introduction of multi-core CPUs, have exacerbated the dependency of CPU performance on the memory subsystem. This trend motivates the search for more efficient caching mechanisms, enabling both faster service of frequently used blocks and decreased power consumption.
Yoav Etsion, Dror G. Feitelson
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Evaluation of a probabilistic hydrometeorological forecast system [PDF]
, 2009 Medium range hydrological forecasts in mesoscale catchments are only possible with the use of hydrological models driven by meteorological forecasts, which in particular contribute quantitative precipitation forecasts (QPF). QPFs are accompanied by large
Jaun, Simon +5 more
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