Results 41 to 50 of about 654,785 (260)
Journal of Agricultural Sciences, 2020 The variables affecting the milk productivity have been discussed in various articles through different methods. A recent study using path analysis shows that three variables significantly affect the 305-day milk yield of Holstein Friesian cows ...
Volkan Sevinç +3 more
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An Adaptive Unsupervised Feature Selection Algorithm Based on MDS for Tumor Gene Data Classification
Sensors, 2021 Identifying the key genes related to tumors from gene expression data with a large number of features is important for the accurate classification of tumors and to make special treatment decisions.
Bo Jin +6 more
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Exact Learning Augmented Naive Bayes Classifier
Entropy, 2021 Earlier studies have shown that classification accuracies of Bayesian networks (BNs) obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the feature variables, were higher than those obtained by maximizing the marginal ...
Shouta Sugahara, Maomi Ueno
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Remote Sensing, 2023 The reconstruction of 3D shapes from a single view has been a longstanding challenge. Previous methods have primarily focused on learning either geometric features that depict overall shape contours but are insufficient for occluded regions, local ...
Guoqing Gao +5 more
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Biological PsychologyThis paper concerns structure learning or discovery of discrete generative models. It focuses on Bayesian model selection and the assimilation of training data or content, with a special emphasis on the order in which data are ingested. A key move - in the ensuing schemes - is to place priors on the selection of models, based upon expected free energy.
Karl J. Friston +12 more
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Learning with structured sparsity
Proceedings of the 26th Annual International Conference on Machine Learning, 2009 This paper investigates a new learning formulation called structured sparsity, which is a natural extension of the standard sparsity concept in statistical learning and compressive sensing. By allowing arbitrary structures on the feature set, this concept generalizes the group sparsity idea that has become popular in recent years.
Huang, Junzhou +2 more
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Atomistic structure learning [PDF]
The Journal of Chemical Physics, 2019 One endeavour of modern physical chemistry is to use bottom-up approaches to design materials and drugs with desired properties. Here we introduce an atomistic structure learning algorithm (ASLA) that utilizes a convolutional neural network to build 2D compounds and layered structures atom by atom.
J��rgensen, Mathias S. +6 more
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An Information Criterion for Inferring Coupling of Distributed Dynamical Systems
Frontiers in Robotics and AI, 2016 The behaviour of many real-world phenomena can be modelled by nonlinear dynamical systems whereby a latent system state is observed through a filter. We are interested in interacting subsystems of this form, which we model by a set of coupled maps as a ...
Oliver Michael Cliff +3 more
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Structured learning modulo theories
Artificial Intelligence, 2017 Modelling problems containing a mixture of Boolean and numerical variables is a long-standing interest of Artificial Intelligence. However, performing inference and learning in hybrid domains is a particularly daunting task. The ability to model this kind of domains is crucial in "learning to design" tasks, that is, learning applications where the goal
Teso, Stefano +2 more
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Knowledge graph construction with structure and parameter learning for indoor scene design
Computational Visual Media, 2018 We consider the problem of learning a representation of both spatial relations and dependencies between objects for indoor scene design. We propose a novel knowledge graph framework based on the entity-relation model for representation of facts in indoor
Yuan Liang +4 more
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