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Restricted Bayesian Network Structure Learning
[chapter]
2004
Studies in Fuzziness and Soft Computing
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As a consequence, researchers have studied learning Bayesian networks with a fixed structure, notably naive Bayesian networks and tree-augmented Bayesian networks, which involves no search at all. ...
Learning the structure of a Bayesian network from data is a difficult problem, as its associated search space is superexponentially large. ...
The FAN algorithm is an example of a restricted Bayesian-network structure learning algorithm [4] . ...
doi:10.1007/978-3-540-39879-0_12
fatcat:gwt7tdyc5fawdpt3l2qyn2hbme
Learning restricted Bayesian network structures
[article]
2010
arXiv
pre-print
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In this paper we deal with the complexity of learning restricted Bayesian network structures, that is, we wish to find network structures of highest score within a given subset of all possible network ...
restricted Bayes network structures. ...
Learning restricted Bayesian network structures A lot of research is devoted to the topic of finding complexity results of the general problem of learning Bayesian network structures analyzing different ...
arXiv:1011.6664v1
fatcat:sfdgoharzrgvjbyqgfmxvsx4wm
Bayesian network learning algorithms using structural restrictions
2007
International Journal of Approximate Reasoning
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The use of several types of structural restrictions within algorithms for learning Bayesian networks is considered. ...
The main goal of this paper is to study whether the algorithms for automatically learning the structure of a Bayesian network from data can obtain better results by using this prior knowledge. ...
learning Bayesian networks. ...
doi:10.1016/j.ijar.2006.06.009
fatcat:fnlxh4okxzfqzidnuebwjtdmn4
An Algorithm for Bayesian Networks Structure Learning Based on Simulated Annealing with MDL Restriction
2008
2008 Fourth International Conference on Natural Computation
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Basically, Bayesian Belief Networks (BBNs) as probabilistic tools provide suitable facilities for modelling process under uncertainty. ...
Finding the beststructure (structure learning) ofthe DAG is a classic NP-Hard problem in BBNs. ...
Bayesian Belief Networks: Bayesian belief networks or BBN is named based on studies of Thomas Bayes (1702-and the parameter learning is a method for learning in 1761) in the field of probability theory ...
doi:10.1109/icnc.2008.658
dblp:conf/icnc/YeCS08
fatcat:72nhkekqmzhyhgn27djeegc7zy
Enabling Large-Scale Bayesian Network Learning by Preserving Intercluster Directionality
2007
IEICE transactions on information and systems
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We propose a recursive clustering and order restriction (R-CORE) method for learning large-scale Bayesian networks. ...
The proposed method considers a reduced search space for directed acyclic graph (DAG) structures in scoring-based Bayesian network learning. ...
The scoring-based learning of a Bayesian network B comprises two parts; learning a DAG structure G and learning probabilistic parameters P. ...
doi:10.1093/ietisy/e90-d.7.1018
fatcat:naeetmbelnexjfwtaybipfmknq
H-CORE: Enabling genome-scale Bayesian analysis of biological systems without prior knowledge
2007
Biosystems (Amsterdam. Print)
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However, the scalability of those approaches is seriously restricted because of the huge search space for finding an optimal DAG structure in the process of Bayesian network learning. ...
In this paper, we use the hierarchical clustering and order restriction (H-CORE) method for the learning of large Bayesian networks by clustering entities and restricting edge directions between those ...
Bayesian network learning
Conventional Bayesian network learning The learning for a Bayesian network B = G, P using given observed data D involves two steps: learning the graph structure G and learning ...
doi:10.1016/j.biosystems.2006.08.004
pmid:17005318
fatcat:cc77pxgeqjaqdfluvj564s2zae
BNFinder: exact and efficient method for learning Bayesian networks
2008
Bioinformatics
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It supports dynamic Bayesian networks and, if the variables are partially ordered, also static Bayesian networks. ...
Results: We present a BNFinder software, which allows for Bayesian network reconstruction from experimental data. ...
., 2004; Meek, 2001) showing that, without restrictive assumptions, learning Bayesian networks from data is NP-hard with respect to the number of network vertices. ...
doi:10.1093/bioinformatics/btn505
pmid:18826957
pmcid:PMC2639006
fatcat:zxa4gmzgznbvff44dmlwnqvjyq
Learning Bayesian network structure based on the classification and regression tree
2008
BMC Neuroscience
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a novel structure learning method to construct the Bayesian network from these data, and the method need not prescribe a previous sequence. ...
In order to overcome limitations of the ordinary algorithm that imposes a previous ordering on the domain attributes that restricts the number of Bayesian structures to be learned, in this paper, we present ...
a novel structure learning method to construct the Bayesian network from these data, and the method need not prescribe a previous sequence. ...
doi:10.1186/1471-2202-9-s1-p71
fatcat:wjccvkbbyrhubhqfrullborgqa
A theory of inferred causation
[chapter]
1995
Studies in Logic and the Foundations of Mathematics
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models: Dynamic Bayesian Networks (DBNs) Continuing Research on Continuing Research on Learning Bayesian Networks from Data Learning Bayesian Networks from Data Kansas State University Department of Computing ...
Kansas State University Department of Computing and Information Sciences CIS 830: Advanced Topics in Artificial Intelligence Learning Bayesian Networks: Learning Bayesian Networks: Missing Observations ...
doi:10.1016/s0049-237x(06)80074-1
fatcat:gihsurzxdjebhkbtilxcjlhtqi
On the Use of Restrictions for Learning Bayesian Networks
[chapter]
2005
Lecture Notes in Computer Science
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In this paper we explore the use of several types of structural restrictions within algorithms for learning Bayesian networks. ...
Bayesian network learning algorithms based on the score+search paradigm. ...
The paper is structured as follows: in Section 2 we briefly give some preliminary basic concepts about learning the structure of Bayesian networks. ...
doi:10.1007/11518655_16
fatcat:bstsaijs55hyvbcvb4ekbl5cpu
Construction of Large-Scale Bayesian Networks by Local to Global Search
[chapter]
2002
Lecture Notes in Computer Science
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Most existing algorithms for structural learning of Bayesian networks are suitable for constructing small-sized networks which consist of several tens of nodes. ...
The proposed method has been evaluated on two benchmark datasets and a real-life DNA microarray data, demonstrating the ability to learn the large-scale Bayesian network structure efficiently. ...
Hence, the task is to learn the structure of Bayesian network with 890 nodes. ...
doi:10.1007/3-540-45683-x_41
fatcat:r672euhaungaldveuwmeltm6s4
Fuzzy Naive Bayesian for constructing regulated network with weights
2015
Bio-medical materials and engineering
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Finally, we will classify the heart and Haberman datasets by the FNB network to compare with experiments of Naive Bayesian and TAN. ...
Therefore, this paper proposes a new algorithm named Fuzzy Naive Bayesian (FNB) using neural network with weighted membership function (NEWFM) to extract regulated relations and weights. ...
However, learning Bayesian network without any restrictions is very time consuming. ...
doi:10.3233/bme-151476
pmid:26405944
fatcat:rzppazgwu5f5ljawnlb4pnyh7u
10.1162/153244304773936045
2000
Applied Physics Letters
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This paper discusses in depth the role that the inclusion order plays in learning the structure of Bayesian networks. ...
Two or more Bayesian network structures are Markov equivalent when the corresponding acyclic digraphs encode the same set of conditional independencies. ...
In the context of structure learning, the Bayesian network structure is often identified as the Bayesian network itself because learning the parameters can be done once the structure has been learned. ...
doi:10.1162/153244304773936045
fatcat:vvsgnjtn5jbppjp36wmya3kkna
An Improved De-noising Algorithm for Bayesian Network Classifiers Parameter Learning
2017
DEStech Transactions on Engineering and Technology Research
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Restricted Bayesian network learning usually consists of two main steps [8, 9] : structure learning and parameters learning. This paper focuses on the study of parameters learning. ...
Based on the Bayesian network parameters learning, this paper redesigns the confidence measure function, and then put forward a kind of noise reduction algorithm which is suitable for the limited structure ...
[12, 13] proposed a cluster based approach for parameters learning in restricted Bayesian networks. ...
doi:10.12783/dtetr/sste2016/6495
fatcat:rhygmxzn7fd2tlfywls3gzcwjy
A Bayesian Network Learning Algorithm Based on Independence Test and Ant Colony Optimization
2009
Acta Automatica Sinica
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To solve the drawbacks of the ant colony optimization for learning Bayesian networks (ACO-B), this paper proposes an improved algorithm based on the conditional independence test and ant colony optimization ...
First, the I-ACO-B uses order-0 independence tests to effectively restrict the space of candidate solutions, so that many unnecessary searches of ants can be avoided. ...
To solve the drawbacks of the ant colony optimization for learning Bayesian networks [6] (ACO-B), this paper proposes a Bayesian network structure learning algorithm based on the conditional independence ...
doi:10.1016/s1874-1029(08)60077-4
fatcat:xfdidmbr4rg7lloh6wcjrlnh3q
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