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Bayesian Structure Adaptation for Continual Learning [article]

Abhishek Kumar, Sunabha Chatterjee, Piyush Rai
2020 arXiv   pre-print
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

Behnaz Moradabadi, Mohammad Mahdi Ebadzadeh, Mohammad Reza Meybodi
2016 Genetic Programming and Evolvable Machines  
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

Jaber El Bouhdidi, Mohamed Ghailani, Abdelhadi Fennan
2013 International Journal of Emerging Technologies in Learning (iJET)  
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]

Luiz Antonio Pereira Silva, João Batista Nunes Bezerra, Mirko Barbosa Perkusich, Kyller Costa Gorgônio, Hyggo Oliveira de Almeida, Angelo Perkusich
2018 Bayesian Networks [Working Title]  
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

Shaojun Wang, Yunxin Zhao
2001 IEEE Transactions on Speech and Audio Processing  
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]

Keum-Sung Hwang, Sung-Bae Cho
2006 Lecture Notes in Computer Science  
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]

Yayi Zou, Zhiwei Qin
2021 arXiv   pre-print
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]

Arthur Arthur Choi, Lu Lu Zheng, Adnan Adnan Darwiche, Ole Mengshoel
2011 Chapman & Hall/CRC Data Mining and Knowledge Discovery Series  
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]

Gladys Castillo, João Gama
2005 Lecture Notes in Computer Science  
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]

Maomi Ueno
2010 E-learning Experiences and Future  
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

Pedro Larrañaga, Hossein Karshenas, Concha Bielza, Roberto Santana
2012 Journal of Heuristics  
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]

Syed Hasib Akhter Faruqui, Adel Alaeddini, Jing Wang, Carlos A. Jaramillo
2021 arXiv   pre-print
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]

Pablo García Bringas, Yoseba K. Penya
2009 Lecture Notes in Business Information Processing  
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

Raziyeh Farmani, Hans Jørgen Henriksen, Dragan Savic
2009 Environmental Modelling & Software  
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

Norbert Dojer, Paweł Bednarz, Agnieszka Podsiadło, Bartek Wilczyński
2013 Computer applications in the biosciences : CABIOS  
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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