Results 1 to 10 of about 1,959,808 (288)
Deep Gaussian Mixture Models [PDF]
Statistics and Computing, 2017 Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed.
McLachlan, Geoffrey J., Viroli, Cinzia
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Mathematics, 2023 The demand for accurate and reliable unsupervised image segmentation methods is high. Regardless of whether we are faced with a problem for which we do not have a usable training dataset, or whether it is not possible to obtain one, we still need to be ...
Branislav Panić +3 more
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Diagnostics, 2022 Dual-energy computed tomography (DECT) is an advanced CT computed tomography scanning technique enabling material characterization not possible with conventional CT scans.
Faicel Chamroukhi +4 more
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Evolutionary Applications, 2022 The sea lamprey (Petromyzon marinus) is an invasive species in the Great Lakes and the focus of a large control and assessment program. Current assessment methods provide information on the census size of spawning adult sea lamprey in a small number of ...
Ellen M. Weise +7 more
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A near‐DC measurement and modelling of low‐frequency noise in electronic components
IET Science, Measurement & Technology, 2023 Low‐frequency noise, generated inherently by the number or mobility fluctuation of carriers, is a crucial concern for the design of analog and digital circuits.
Zeinab Shamaee +2 more
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A shift in strategy or “error”? Strategy classification over multiple stochastic specifications [PDF]
Judgment and Decision Making, 2011 We present a classification methodology that jointly assigns to a decision maker a best-fitting decision strategy for a set of choice data as well as a best-fitting stochastic specification of that decision strategy.
Clintin P. Davis-Stober +3 more
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Effect of Mesotrione and Nicosulfuron Mixtures With or Without Adjuvants [PDF]
Planta Daninha, 2018 : In Field experiments, a logarithmic sprayer was used to screen the efficacy of 28.5% mixture of nicosulfuron and mesotrione, and the herbicides applied separately.
J. DUUS, N.D. KRUSE, J.C. STREIBIG
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Mixture-Based Probabilistic Graphical Models for the Label Ranking Problem
Entropy, 2021 The goal of the Label Ranking (LR) problem is to learn preference models that predict the preferred ranking of class labels for a given unlabeled instance. Different well-known machine learning algorithms have been adapted to deal with the LR problem. In
Enrique G. Rodrigo +3 more
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Journal of Statistical Software, 2021 We introduce the new package dmbc that implements a Bayesian algorithm for clustering a set of binary dissimilarity matrices within a model-based framework.
Sergio Venturini, Raffaella Piccarreta
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Graph Laplacian Mixture Model [PDF]
IEEE Transactions on Signal and Information Processing over Networks, 2020 Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all available data relate to the same graph.
Hermina Petric Maretic, Pascal Frossard
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