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Bayesian Structure Adaptation for Continual Learning
[article]
2020
arXiv
pre-print
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structure of deep networks to adapt to new tasks. ...
We present a novel Bayesian approach to continual learning based on learning the structure of deep neural networks, addressing the shortcomings of both these approaches. ...
We leave these extensions for future work.
Supplementary Material Bayesian Structure Adaptation for Continual Learning
A. ...
arXiv:1912.03624v2
fatcat:bk4nd7dazjhizhaj2dbzogo23i
A new real-coded stochastic Bayesian optimization algorithm for continuous global optimization
2016
Genetic Programming and Evolvable Machines
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A new real-coded stochastic Bayesian optimization algorithm for continuous global optimization... http://A new real-coded stochastic Bayesian optimization algorithm for continuous global optimization.. ...
A new real-coded stochastic Bayesian optimization algorithm for continuous global optimization... ...
of learning automata for continuous Wei Bin · Peng QinKe · Chen Xiao et al in Chinese Science What is this? ...
doi:10.1007/s10710-015-9255-3
fatcat:nqy4rtgrjjfihppo3576o44hce
A Probabilistic Approach for the Generation of Learning Sessions Tailored to the Learning Styles of Learners
2013
International Journal of Emerging Technologies in Learning (iJET)
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In this paper we present an audacious solution based on Bayesian networks and educational approach for the construction of evolutionary personalized learning paths. ...
networks that calculates the probability of success of each candidate hypermedia unit, for selecting those who are most appropriate for the construction of evolutionary personalized learning paths. ...
Structure of Bayesian Model The Bayesian model that we propose is a causal network, his structure is defined based on discrete and continuous variables representing the characteristics of the learner, ...
doi:10.3991/ijet.v8i6.3084
fatcat:3smfuip65vfppa54nq7xnnbyde
Continuous Learning of the Structure of Bayesian Networks: A Mapping Study
[chapter]
2018
Bayesian Networks [Working Title]
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In regard to the continuous learning of the Bayesian network's structure, the current solutions are based on its structural refinement or adaptation. ...
This study aims to identify and evaluate solutions for the continuous learning of the Bayesian network's structures, as well as to outline related future research directions. ...
Acknowledgements The authors would like to thank Federal University of Campina Grande in Brazil for supporting this study. ...
doi:10.5772/intechopen.80064
fatcat:tf2sutw7yfg45owi4vl6ouvrru
Online Bayesian tree-structured transformation of HMMs with optimal model selection for speaker adaptation
2001
IEEE Transactions on Speech and Audio Processing
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This paper presents a new recursive Bayesian learning approach for transformation parameter estimation in speaker adaptation. ...
An online Bayesian learning technique is proposed for recursive maximum a posteriori (MAP) estimation of LR and affine transformation parameters. ...
Mokbel for providing their preprints. Finally, they thank J. J. Liu of Beckman Institute, University of Illinois at Urbana-Champaign, for a helpful discussion about Bayesian model selection. ...
doi:10.1109/89.943344
fatcat:wqakgiaxrjdcnovxeql5ulnzwq
Interactive Learning of Scene Context Extractor Using Combination of Bayesian Network and Logic Network
[chapter]
2006
Lecture Notes in Computer Science
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The logic network works for supporting logical inference of Bayesian network. ...
In the result of some learning experiments using interactive data, we have confirmed that the proposed interactive learning method is useful for scene context reasoning. ...
For this reason, they are difficult to adapt several interactive data effectively added by a user-feedback. There were researches for learning the Bayesian model. ...
doi:10.1007/11864349_104
fatcat:s2yamyuxl5enhj5hrnjfuuqrfq
Bayesian Meta-reinforcement Learning for Traffic Signal Control
[article]
2021
arXiv
pre-print
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This framework is based on our proposed fast-adaptation variation to Gradient-EM Bayesian Meta-learning and the fast-update advantage of DQN, which allows for fast adaptation to new scenarios with continual ...
continual learning ability in heterogeneous scenarios. ...
First, we propose a Bayesian meta-learning algorithm BM-DQN with robust continual learning ability. Unlike previous methods, e.g. ...
arXiv:2010.00163v2
fatcat:zb3oampw5ffedit77rlzegowkm
A Tutorial on Bayesian Networks for Systems Health Management
[chapter]
2011
Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
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For more on learning Bayesian networks from data, including how to learn the structure of a Bayesian network, we recommend [8, 17] . ...
Continuous data, including continuous sensor readings, were discretized before being used for clamping the appropriate discrete random variables in the ADAPT Bayesian network. ...
doi:10.1201/b11580-4
fatcat:5w22brb5gjbcleiisen3vqv6vi
Bias Management of Bayesian Network Classifiers
[chapter]
2005
Lecture Notes in Computer Science
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The purpose of this paper is to describe an adaptive algorithm for improving the performance of Bayesian Network Classifiers (BNCs) in an on-line learning framework. ...
Results in conducted experiments using the class of Dependence Bayesian Classifiers on three large datasets show that our algorithm is able to select a model with the appropriate complexity for the current ...
The Adaptive Algorithm for Learning k-DBCs In this section we describe our adaptive algorithm for learning k-DBCs in an online framework. ...
doi:10.1007/11563983_8
fatcat:ym2gjahskfhgvateblmo4a4sye
Bayesian Agent in E-learning
[chapter]
2010
E-learning Experiences and Future
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The Bayesian-network model can avoid the cold-start problem by providing a valid prior belief for the network structure, even if there are not sufficiently large amounts of data for learning a Bayesian ...
Fifteen Bayesian network structures are estimated corresponding to learners' learning histories data for the fifteen weeks because all courses run for 15 weeks. ...
/bayesian-agent-in-e-learning ...
doi:10.5772/8815
fatcat:bstyhi7qbbhsxde75iimldeoca
A review on probabilistic graphical models in evolutionary computation
2012
Journal of Heuristics
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Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. ...
Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. ...
. , 2000b learns the chain structured probabilistic model for continuous variables by adapting the concept of (conditional) entropy for univariate and bivariate Gaussian distributions. ...
doi:10.1007/s10732-012-9208-4
fatcat:54ipbzsryfbt5nqmaczgurb2he
A Functional Model for Structure Learning and Parameter Estimation in Continuous Time Bayesian Network: An Application in Identifying Patterns of Multiple Chronic Conditions
[article]
2021
arXiv
pre-print
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We also propose an adaptive regularization method with an intuitive early stopping feature based on density based clustering for efficient learning of the structure and parameters of the proposed network ...
Here, we propose a continuous time Bayesian network with conditional dependencies, represented as Poisson regression, to model the impact of exogenous variables on the conditional dependencies of the network ...
ADAPTIVE GROUP REGULARIZATION FRAMEWORK FOR STRUCTURE LEARNING IN CONTINUOUS TIME BAYESIAN NETWORK The parameter estimation approach presented above requires the parent set of each condition to be known ...
arXiv:2007.15847v2
fatcat:wx6s5ggdrbc55bwupnz4cyf2kq
Next-Generation Misuse and Anomaly Prevention System
[chapter]
2009
Lecture Notes in Business Information Processing
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The training process of the Bayesian network may become intractable very fast in some extreme situations; we present also a method to cope with this problem. ...
Against this, we present here ESIDE-Depian, the first unified misuse and anomaly prevention system based on Bayesian Networks to analyse completely network packets, and the strategy to create a consistent ...
Also thanks to the Regional Government of Biscay (Bizkaiko Foru Aldundia) and the Basque Government (Eusko Jaurlaritza) for their financial support. ...
doi:10.1007/978-3-642-00670-8_9
fatcat:b6kz3udupzcxhamd2qmqjae4by
An evolutionary Bayesian belief network methodology for optimum management of groundwater contamination
2009
Environmental Modelling & Software
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This paper presents an approach for constructing and testing a decision analysis process for adaptive water management under uncertainty. ...
The proposed methodology addresses this major shortcoming of Bayesian belief networks. ...
ACKNOWLEDGEMENTS This work was supported by the European Commission under the contract No 511179 for the Integrated Project NeWater. ...
doi:10.1016/j.envsoft.2008.08.005
fatcat:quxsqcvilnfyhkd4ytlhelknx4
BNFinder2: Faster Bayesian network learning and Bayesian classification
2013
Computer applications in the biosciences : CABIOS
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The improvements include (i) a parallelized learning algorithm leading to an order of magnitude speed-ups in BN structure learning time; (ii) inclusion of an additional scoring function based on mutual ...
BNFinder is a fast software implementation of an exact algorithm for finding the optimal structure of the network given a number of experimental observations. ...
While BNFinder uses an efficient algorithm for BN structure learning, the original implementation was limited to running on a single CPU due to the limitations of the Python interpreter. ...
doi:10.1093/bioinformatics/btt323
pmid:23818512
pmcid:PMC3722519
fatcat:xqd6ztbxv5gfdi7scgaw4ldxqy
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