Results 61 to 70 of about 21,280,601 (295)
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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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
doaj +1 more source
Exploiting Cognitive Structure for Adaptive Learning
, 2019 Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner.
Chang H.-S. +8 more
core +1 more source
The dynamics of syntax acquisition: facilitation between syntactic structures [PDF]
, 2011 This paper sets out to show how facilitation between different clause structures operates over time in syntax acquisition. The phenomenon of facilitation within given structures has been widely documented, yet inter-structure facilitation has rarely been
Ben-Horin +15 more
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Structure Learning in Nested Effects Models
, 2008 Nested Effects Models (NEMs) are a class of graphical models introduced to analyze the results of gene perturbation screens. NEMs explore noisy subset relations between the high-dimensional outputs of phenotyping studies, e.g. the effects showing in gene
Markowetz, Florian, Tresch, Achim
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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
openaire +4 more sources