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    188宝金博页面版: Physarum Learner： A Slime Mold Inspired Structural Learning Approach

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    内容提示： Physarum Learner: A Slime Mold InspiredStructural Learning ApproachT. Sch?n, M. Stetter, O. Belova, A. Koch, A.M. Tomé and E.W. LangAbstract A novel Score-based Physarum Learner algorithm for learning BayesianNetwork structure from data is introduced and shown to outperform common scorebased structure learning algorithms for some benchmark data sets. The Score-basedPhysarum Learner f i rst initializes a fully connected Physarum-Maze with randomconductances. In each Physarum Solver iteration, the source a...

    文档格式：PDF | 页数：29 | 浏览次数：39 | 上传日期：2018-04-18 13:04:55 | 文档星级：
    Physarum Learner: A Slime Mold InspiredStructural Learning ApproachT. Schön, M. Stetter, O. Belova, A. Koch, A.M. Tomé and E.W. LangAbstract A novel Score-based Physarum Learner algorithm for learning BayesianNetwork structure from data is introduced and shown to outperform common scorebased structure learning algorithms for some benchmark data sets. The Score-basedPhysarum Learner f i rst initializes a fully connected Physarum-Maze with randomconductances. In each Physarum Solver iteration, the source and sink nodes arechanged randomly, and the conductances are updated. Connections exceeding a pre-def i ned conductance threshold are considered as Bayesian Network edges, and thescore of the connected nodes are examined in both directions. A positive or nega-tive feedback is given to the edge conductance based on the calculated scores. Dueto randomness in selecting connections for evaluation, an ensemble of Score-basedPhysarum Learner is used to build the f i nal Bayesian Network structure.1 IntroductionStudying information processing in simple cellular organisms helps to learn aboutsolving combinatorial optimization problems. The slime mold Physarum poly-cephalum has emerged recentlyasafascinatinglearningparadigm ofhowbiologicalsystems solve NP-hard problems [19] such as shortest path f i nding and learningstructurefromdata.Thslimemold’scomputationalabilitythuscouldhelpdesigningnew methods of computation. Through its growth process, this single cell organismT. Schön · O. Belova · A. Koch · E.W. Lang ( B )CIML Lab, Department of Biophysics, University of Regensburg, Regensburg, Germanye-mail: elmar.lang@ur.deM. StetterDepartment of Bioinformatics, University of Applied ScienceWeihenstephan-Triesdorf, Freising, Germanye-mail: martin.stetter@hswt.deA.M. ToméIEETA, Department of Electrical Engineering,Telecommunications and Informatics, Universidade de Aveiro, Aveiro, Portugale-mail: ana@ua.pt© Springer International Publishing Switzerland 2016A. Adamatzky (ed.), Advances in Physarum Machines, Emergence,Complexity and Computation 21, DOI 10.1007/978-3-319-26662-6_25489

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