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
Unsupervised Dynamic Discrete Structure Learning: A Geometric Evolution Method
Sign Up to like & getrecommendations! Published in 2025 at "IEEE/CAA Journal of Automatica Sinica"
DOI: 10.1109/jas.2025.125165
Abstract: Revealing the latent low-dimensional geometric structure of high-dimensional data is a crucial task in unsupervised representation learning. Traditional manifold learning, as a typical method for discovering latent geometric structures, has provided important nonlinear insight for… read more here.
Keywords: method; structure; representation learning; geometric structure ... 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
You Never Walk Alone: A Generalizable and Nonparametric Structure Learning Framework
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2025.3580101
Abstract: The graph-structured learning (GSL) aims to assist graph neural networks (GNNs) to yield effective node embeddings for downstream tasks, especially in scenarios with the absence of structures or the existence of unreliable edges. Most GSL… read more here.
Keywords: structure; learning framework; nonparametric structure; generalizable nonparametric ... See more keywords
A Unified Framework for Robust Encrypted Malicious Traffic Detection in Adverse Environments via Graph Structure Learning
Sign Up to like & getrecommendations! Published in 2026 at "IEEE Transactions on Network Science and Engineering"
DOI: 10.1109/tnse.2025.3582871
Abstract: The widespread adoption of encryption protocols enables attackers to conceal malicious activities within encrypted traffic, rendering traditional detection methods ineffective. Graph Neural Networks (GNNs) have emerged as a promising solution by modeling network objects and… read more here.
Keywords: detection; adverse environments; graph structure; traffic ... See more keywords