Particle Learning Methods for State and Parameter Estimation [PDF]
, 2012 This paper presents an approach for online parameter estimation within particle lters. Current research has mainly been focused towards the estimation of static parameters.
Fearnhead, Paul +3 more
core +5 more sources
Expanding the notion of global learning: Turkish-Dutch teens’ networked configurations for learning [PDF]
, 2020 Digital technology facilitate interactions between learners and resources at a global level. New learner prototypes are therefore proposed, such as the notion of the global learner.
Ünlüsoy, Asli +5 more
core +2 more sources
BN parameter learning based on improved QMAP algorithm under small data set conditions
Xi'an Gongcheng Daxue xuebao, 2023 Under the condition of Bayesian network (BN) small data set, the qualitative maximum a posteriori (QMAP) estimation tends to violate expert constraints, which causes the QMAP estimation to deviate the true value.
CHEN Haiyang +3 more
doaj +1 more source
Hard and Soft EM in Bayesian Network Learning from Incomplete Data
Algorithms, 2020 Incomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their graphical and causal interpretations.
Andrea Ruggieri +3 more
doaj +1 more source
Reply to determining structural identifiability of parameter learning machines [PDF]
, 2016 The paper Ran and Hu (2014, Neurocomputing) examines identifiability and parameter redundancy in classes of models used in machine learning. This note discusses the results on global identifiability and also clarifies that the paper's results on ...
Cole, Diana J.
core +1 more source
DISCIPLINE ATTITUDE OF ELEMENTARY SCHOOL STUDENTS ON DISTANCE LEARNING [PDF]
, 2022 The Covid-19 pandemic has hit Indonesia for about a year, and many activities have shifted online. No exception in the field of education. Learning is done online or in distance learning.
Hadi, Waluyo +3 more
core +1 more source
Process Monitoring Based on Multivariate Causality Analysis and Probability Inference
IEEE Access, 2018 System security is one of the key challenges of the cyber-physical systems. Bayesian approach can estimate and predict the potentially harmful factors of the general system, but it has many limitations that can lead to undesirable effects in the complex ...
Xiaolu Chen, Jing Wang, Jinglin Zhou
doaj +1 more source
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
doaj +1 more source
Federated learning with hyper-parameter optimization
Journal of King Saud University: Computer and Information Sciences, 2023 Federated Learning is a new approach for distributed training of a deep learning model on data scattered across a large number of clients while ensuring data privacy.
Majid Kundroo, Taehong Kim
doaj +1 more source
A parameter-free learning automaton scheme
Frontiers in Neurorobotics, 2022 For a learning automaton, a proper configuration of the learning parameters is crucial. To ensure stable and reliable performance in stochastic environments, manual parameter tuning is necessary for existing LA schemes, but the tuning procedure is time ...
Xudie Ren, Shenghong Li, Hao Ge
doaj +1 more source