Results 21 to 30 of about 2,499,784 (284)
Missing.... presumed at random: cost-analysis of incomplete data [PDF]
, 2002 When collecting patient-level resource use data for statistical analysis, for some patients and in some categories of resource use, the required count will not be observed. Although this problem must arise in most reported economic evaluations containing
Bang +31 more
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Handling incomplete heterogeneous data using VAEs [PDF]
Pattern Recognition, 2020 Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate for capturing the latent structure of vast amounts of complex high-dimensional data. However, existing VAEs can still not directly handle data that are heterogenous (mixed continuous and discrete) or incomplete (with missing data at random),
Alfredo Nazábal +3 more
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Uncovering Suspicious Activity From Partially Paired and Incomplete Multimodal Data
IEEE Access, 2017 Multimodal data can be used to gain additional perspective on a phenomenon. For applications, such as security and the detection of suspicious activity, the need to aggregate and analyze data from multiple modes is vital.
Carter Chiu, Justin Zhan, Felix Zhan
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Electron density retrieval from truncated Radio Occultation GNSS data [PDF]
, 2019 This paper summarizes the definition and validation of two complementary new strategies, to invert incomplete Global Navigation Satellite System Radio-Occultation (RO) ionospheric measurements, such as the ones to be provided by the future EUMETSAT Polar
Cardellach Galí, Estel +3 more
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On Classification with Incomplete Data [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007 We address the incomplete-data problem in which feature vectors to be classified are missing data (features). A (supervised) logistic regression algorithm for the classification of incomplete data is developed. Single or multiple imputation for the missing data is avoided by performing analytic integration with an estimated conditional density function
David, Williams +4 more
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Certainty Closure: Reliable Constraint Reasoning with Incomplete or Erroneous Data [PDF]
, 2006 Constraint Programming (CP) has proved an effective paradigm to model and solve difficult combinatorial satisfaction and optimisation problems from disparate domains. Many such problems arising from the commercial world are permeated by data uncertainty.
Benhamou F. +26 more
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Analysis of Incomplete Data and an Intrinsic-Dimension Helly Theorem [PDF]
, 2008 The analysis of incomplete data is a long-standing challenge in practical statistics. When, as is typical, data objects are represented by points in R^d , incomplete data objects correspond to affine subspaces (lines or Δ-flats).With this motivation we ...
Gao, Jie +2 more
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Determining the Macroscopic Fundamental Diagram from Mixed and Partial Traffic Data
Promet (Zagreb), 2018 The macroscopic fundamental diagram (MFD) is a graphical method used to characterize the traffic state in a road network and to monitor and evaluate the effect of traffic management.
Yangbeibei Ji +3 more
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Applied Sciences, 2022 Feature selection has been widely used in machine learning and data mining since it can alleviate the burden of the so-called curse of dimensionality of high-dimensional data.
Jun Cai, Linge Fan, Xin Xu, Xinrong Wu
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, 2021 This chapter discusses missing-value problems from the perspective of machine learning. Missing values frequently occur during data acquisition. When a dataset contains missing values, nonvectorial data are generated. This subsequently causes a serious problem in pattern recognition models because nonvectorial data need further data wrangling before ...
Bo-Wei, Chen,, Jia-Ching, Wang,
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