Discriminative boosted Bayes networks for learning multiple cardiovascular procedures

2017 ◽  
Author(s):  
Nandini Ramanan ◽  
Shuo Yang ◽  
Shaun Grannis ◽  
Sriraam Natarajan
Keyword(s):  
Bayes Networks

Introduction to Bayes Networks

2016 ◽  
pp. 459-463
Author(s):  
Rudolf Kruse ◽  
Christian Borgelt ◽  
Christian Braune ◽  
Sanaz Mostaghim ◽  
Matthias Steinbrecher
Keyword(s):  
Bayes Networks

scholarly journals Machine Learning Based on Bayes Networks to Predict the Cascading Failure Propagation

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 44815-44823 ◽  
Author(s):  
Renjian Pi ◽  
Ye Cai ◽  
Yong Li ◽  
Yijia Cao
Keyword(s):  
Machine Learning ◽  
Cascading Failure ◽  
Failure Propagation ◽  
Bayes Networks

FDA -A Scalable Evolutionary Algorithm for the Optimization of Additively Decomposed Functions

1999 ◽  
Vol 7 (4) ◽  
pp. 353-376 ◽  
Author(s):  
Heinz Mühlenbein ◽  
Thilo Mahnig
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithm ◽  
Discrete Distribution ◽  
Grid Structure ◽  
Approximate Factorization ◽  
Marginal Distributions ◽  
A Chain ◽  
Bayes Networks ◽  
Specific Assignment ◽  
Discrete Functions

The Factorized Distribution Algorithm (FDA) is an evolutionary algorithm which combines mutation and recombination by using a distribution. The distribution is estimated from a set of selected points. In general, a discrete distribution defined for n binary variables has 2n parameters. Therefore it is too expensive to compute. For additively decomposed discrete functions (ADFs) there exist algorithms which factor the distribution into conditional and marginal distributions. This factorization is used by FDA. The scaling of FDA is investigated theoretically and numerically. The scaling depends on the ADF structure and the specific assignment of function values. Difficult functions on a chain or a tree structure are solved in about O(n√n) operations. More standard genetic algorithms are not able to optimize these functions. FDA is not restricted to exact factorizations. It also works for approximate factorizations as is shown for a circle and a grid structure. By using results from Bayes networks, FDA is extended to LFDA. LFDA computes an approximate factorization using only the data, not the ADF structure. The scaling of LFDA is compared to the scaling of FDA.

Wigmore Diagrams and Bayes Networks

1993 ◽  
pp. 93-98
Author(s):  
G. A. Vignaux ◽  
Bernard Robertson
Keyword(s):  
Bayes Networks

Introduction to Bayes Networks

2013 ◽  
pp. 409-413
Author(s):  
Rudolf Kruse ◽  
Christian Borgelt ◽  
Frank Klawonn ◽  
Christian Moewes ◽  
Matthias Steinbrecher ◽  
...  
Keyword(s):  
Bayes Networks
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