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A Fast Calculation of Metric Scores for Learning Bayesian Network

Lv, Qiang ; Xia, Xiao-Yan ; Qian, Pei-De

International journal of automation and computing, 2012-02, Vol.9 (1), p.37-44 [Periódico revisado por pares]

Heidelberg: Institute of Automation, Chinese Academy of Sciences

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  • Título:
    A Fast Calculation of Metric Scores for Learning Bayesian Network
  • Autor: Lv, Qiang ; Xia, Xiao-Yan ; Qian, Pei-De
  • Assuntos: Bayesian analysis ; CAE) and Design ; Computer Applications ; Computer-Aided Engineering (CAD ; Control ; Counting ; Engineering ; Learning ; Machine learning ; Mathematical analysis ; Mathematical models ; Mechatronics ; Networks ; Robotics ; State of the art ; 二级缓存 ; 公制 ; 分数 ; 学习任务 ; 快速计算方法 ; 数字系统 ; 机器学习算法 ; 贝叶斯网络
  • É parte de: International journal of automation and computing, 2012-02, Vol.9 (1), p.37-44
  • Notas: Frequent counting is a very so often required operation in machine learning algorithms. A typical machine learning task, learning the structure of Bayesian network （BN） based on metric scoring, is introduced as an example that heavily relies on frequent counting. A fast calculation method for frequent counting enhanced with two cache layers is then presented for learning BN. The main contribution of our approach is to eliminate comparison operations for frequent counting by introducing a multi-radix number system calculation. Both mathematical analysis and empirical comparison between our method and state-of-the-art solution are conducted. The results show that our method is dominantly superior to state-of-the-art solution in solving the problem of learning BN.
    11-5350/TP
    Frequent counting, radix-based calculation, ADtree, learning Bayesian network, metric score
    ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: Frequent counting is a very so often required operation in machine learning algorithms. A typical machine learning task, learning the structure of Bayesian network （BN） based on metric scoring, is introduced as an example that heavily relies on frequent counting. A fast calculation method for frequent counting enhanced with two cache layers is then presented for learning BN. The main contribution of our approach is to eliminate comparison operations for frequent counting by introducing a multi-radix number system calculation. Both mathematical analysis and empirical comparison between our method and state-of-the-art solution are conducted. The results show that our method is dominantly superior to state-of-the-art solution in solving the problem of learning BN.
  • Editor: Heidelberg: Institute of Automation, Chinese Academy of Sciences
  • Idioma: Chinês;Inglês
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