Results 11 to 20 of about 8,034,918 (294)
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
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Structure Learning via Parameter Learning [PDF]
Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 2014 A key challenge in information and knowledge management is to automatically discover the underlying structures and patterns from large collections of extracted information. This paper presents a novel structure-learning method for a new, scalable probabilistic logic called ProPPR.
William Yang Wang +2 more
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Meta-Ensemble Parameter Learning
CoRR, 2022 Ensemble of machine learning models yields improved performance as well as robustness. However, their memory requirements and inference costs can be prohibitively high. Knowledge distillation is an approach that allows a single model to efficiently capture the approximate performance of an ensemble while showing poor scalability as demand for re ...
Zhengcong Fei +4 more
openaire +2 more sources
Ensemble Learning for Mapper Parameter Optimization [PDF]
2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI), 2023 The Mapper algorithm is a technique from TDA used to create low-dimensional graph-based representations of high-dimensional data, proven effective in numerous exploratory data analysis tasks. The Mapper algorithm’s output depends on several user-chosen parameters, and selecting their values is a non-trivial choice, significantly narrowing its potential
Padraig Fitzpatrick +3 more
openaire +3 more sources
A Study of Using Bethe/Kikuchi Approximation for Learning Directed Graphic Models
IEEE Access, 2021 This paper applies the variational methods to learn the parameters and the probability of evidence of directed graphic models (also known as Bayesian networks (BNs)) when data contains missing values.
Peng Lin, Martin Neil, Norman Fenton
doaj +1 more source
IEEE Access, 2020 Recent advances have illustrated substantial benefits from learning Bayesian networks (BNs). However, when the available data size is small, the BN parameter learning becomes a key challenge in many intelligent applications.
Yongyan Hou +4 more
doaj +1 more source
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
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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