Results 11 to 20 of about 2,499,784 (284)
Hard and Soft EM in Bayesian Network Learning from Incomplete Data
Algorithms, 2020 Incomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their graphical and causal interpretations.
Andrea Ruggieri +3 more
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Vietnam Journal of Computer Science, 2023 In intelligent information systems data play a critical role. The issue of missing data is one of the commonplace problems occurring in data collected in the real world. The problem stems directly from the very nature of data collection.
Mateusz Szczepański +3 more
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Enhanced PSSV for Incomplete Data Analysis
IEEE Access, 2020 Partial Sum Minimization of Singular Values (PSSV) is a powerful tool for image denoising, matrix completion and recovering underlying low-rank structure from the corrupted data via Partial Sum Minimization of Singular Values. However, the performance of
Weimin Hou, Qin Li
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Ensemble-based Top-k Recommender System Considering Incomplete Data [PDF]
Journal of Artificial Intelligence and Data Mining, 2019 Recommender systems have been widely used in e-commerce applications. They are a subclass of information filtering system, used to either predict whether a user will prefer an item (prediction problem) or identify a set of k items that will be user ...
M. Moradi, J. Hamidzadeh
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Development of models and assessment of the world walnut variety fund by fruit quality
Новые технологии, 2023 Walnut (Juglans regia L.) is a particularly significant plant for humans in terms of its useful properties and in the Russian Federation it can be attributed to the most valuable introducers for forestry and horticulture. It is grown in many countries of
S. G. Biganova +3 more
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Fuzzy Prognosis System for Decision Making to Vibrations Monitoring in Gas Turbine
Journal of Mechanical Engineering, 2021 This paper proposes a decision making approach based on the development of a fuzzy prognostic system to ensure the vibrations monitoring of a gas turbine based on real time information obtained from different installed sensors.
Boulanouar Saadat +3 more
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Bayesian Robust Tensor Factorization for Incomplete Multiway Data [PDF]
, 2015 We propose a generative model for robust tensor factorization in the presence of both missing data and outliers. The objective is to explicitly infer the underlying low-CP-rank tensor capturing the global information and a sparse tensor capturing the ...
Amari, Shun-ichi +4 more
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Sensors, 2018 In wireless sensor networks, the classification of incomplete data reported by sensor nodes is an open issue because it is difficult to accurately estimate the missing values.
Yang Zhang +4 more
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Proceedings of the 33rd ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, 2014 Incomplete data is ubiquitous: the more data we accumulate and the more widespread tools for integrating and exchanging data become, the more instances of incompleteness we have. And yet the subject is poorly handled by both practice and theory. Many queries for which students get full marks in their undergraduate courses will not work correctly in the
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Quantum Correlations from the Conditional Statistics of Incomplete Data [PDF]
, 2016 We study, in theory and experiment, the quantum properties of correlated light fields measured with click-counting detectors providing incomplete information on the photon statistics.
Barbieri, M. +7 more
core +2 more sources