Bayesian network structure learning with improved genetic algorithm
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22833
Abstract: As an important model of machine learning, Bayesian networks (BNs) have received a lot of attentions since they can be used for classification via probabilistic inference. However, since it is a complicated combination optimization problem,… read more here.
Keywords: structure; structure learning; genetic algorithm; network structure ... See more keywords
Non-Gaussian Methods for Causal Structure Learning
Sign Up to like & getrecommendations! Published in 2018 at "Prevention Science"
DOI: 10.1007/s11121-018-0901-x
Abstract: Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be… read more here.
Keywords: structure learning; non gaussian; causal; causal structure ... See more keywords
Successful structure learning from observational data
Sign Up to like & getrecommendations! Published in 2018 at "Cognition"
DOI: 10.1016/j.cognition.2018.06.003
Abstract: Previous work suggests that humans find it difficult to learn the structure of causal systems given observational data alone. We identify two conditions that enable successful structure learning from observational data: people succeed if the… read more here.
Keywords: structure learning; structure; learning observational; successful structure ... See more keywords
Latent structure learning as an alternative computation for group inference
Sign Up to like & getrecommendations! Published in 2022 at "Behavioral and Brain Sciences"
DOI: 10.1017/s0140525x21001254
Abstract: Abstract In contrast to Pietraszewski's account, latent structure learning neither requires conflict nor relies on observation of explicit coalitional behavior to support group inference. This alternative addresses how even non-conflict-based groups may be defined and… read more here.
Keywords: group inference; structure learning; latent structure;
Self-Supervised Structure Learning for Crack Detection Based on Cycle-Consistent Generative Adversarial Networks
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Computing in Civil Engineering"
DOI: 10.1061/(asce)cp.1943-5487.0000883
Abstract: AbstractDeep learning is a state-of-the-art approach to pixel-level crack detection. However, it relies on a large number of source–target image pairs for the training, which is very expensive. Thi... read more here.
Keywords: self supervised; structure learning; crack detection; supervised structure ... See more keywords
A Bayesian Network Structure Learning Algorithm Based on Probabilistic Incremental Analysis and Constraint
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3229128
Abstract: To address the problem of low efficiency of the existing hill-climbing algorithm in Bayesian network structure learning, this paper proposes a Bayesian network structure learning algorithm based on probabilistic incremental analysis and constraints. The algorithm… read more here.
Keywords: network; structure learning; network structure; learning algorithm ... See more keywords
Data-Driven I/O Structure Learning With Contemporaneous Causality
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Control of Network Systems"
DOI: 10.1109/tcns.2020.3015021
Abstract: In the era of big data, industry and public policy are able to make use of large amounts of data for policy decisions. The proliferation of cheap sensors and fast communication enables policy makers to… read more here.
Keywords: driven structure; learning contemporaneous; structure learning; structure ... See more keywords
Intrinsic and Complete Structure Learning Based Incomplete Multiview Clustering
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Multimedia"
DOI: 10.1109/tmm.2021.3138638
Abstract: In the real-world, some views of samples are often missing for the collected multiview data. Faced with the incomplete multiview data, most of the existing clustering methods tended to learn a common graph from the… read more here.
Keywords: complete structure; structure; structure learning; multiview ... See more keywords
Differentiating between Bayesian parameter learning and structure learning based on behavioural and pupil measures
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DOI: 10.1371/journal.pone.0270619
Abstract: Within predictive processing two kinds of learning can be distinguished: parameter learning and structure learning. In Bayesian parameter learning, parameters under a specific generative model are continuously being updated in light of new evidence. However,… read more here.
Keywords: learning structure; parameter learning; structure learning; phase ... See more keywords
Structure learning enhances concept formation in synthetic Active Inference agents
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DOI: 10.1371/journal.pone.0277199
Abstract: Humans display astonishing skill in learning about the environment in which they operate. They assimilate a rich set of affordances and interrelations among different elements in particular contexts, and form flexible abstractions (i.e., concepts) that… read more here.
Keywords: learning enhances; structure; structure learning; active inference ... See more keywords
BDgraph: An R Package for Bayesian Structure Learning in Graphical Models
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Statistical Software"
DOI: 10.18637/jss.v089.i03
Abstract: Graphical models provide powerful tools to uncover complicated patterns in multivariate data and are commonly used in Bayesian statistics and machine learning. In this paper, we introduce the R package BDgraph which performs Bayesian structure… read more here.
Keywords: package; structure learning; bayesian structure; bdgraph package ... See more keywords